diff --git a/notebooks/how_to/explore_tests.ipynb b/notebooks/how_to/explore_tests.ipynb
index 672c98fc3..73b548fc7 100644
--- a/notebooks/how_to/explore_tests.ipynb
+++ b/notebooks/how_to/explore_tests.ipynb
@@ -81,1786 +81,2178 @@
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#T_0502a_row194_col6, #T_0502a_row194_col7, #T_0502a_row194_col8 {\n",
               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_87ae0\">\n",
+              "<table id=\"T_0502a\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_87ae0_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_87ae0_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_87ae0_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_87ae0_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_87ae0_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
-              "      <th id=\"T_87ae0_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
-              "      <th id=\"T_87ae0_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
+              "      <th id=\"T_0502a_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
+              "      <th id=\"T_0502a_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
+              "      <th id=\"T_0502a_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+              "      <th id=\"T_0502a_level0_col3\" class=\"col_heading level0 col3\" >Has Figure</th>\n",
+              "      <th id=\"T_0502a_level0_col4\" class=\"col_heading level0 col4\" >Has Table</th>\n",
+              "      <th id=\"T_0502a_level0_col5\" class=\"col_heading level0 col5\" >Required Inputs</th>\n",
+              "      <th id=\"T_0502a_level0_col6\" class=\"col_heading level0 col6\" >Params</th>\n",
+              "      <th id=\"T_0502a_level0_col7\" class=\"col_heading level0 col7\" >Tags</th>\n",
+              "      <th id=\"T_0502a_level0_col8\" class=\"col_heading level0 col8\" >Tasks</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_87ae0_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.ACFandPACFPlot</td>\n",
-              "      <td id=\"T_87ae0_row0_col1\" class=\"data row0 col1\" >AC Fand PACF Plot</td>\n",
-              "      <td id=\"T_87ae0_row0_col2\" class=\"data row0 col2\" >Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...</td>\n",
-              "      <td id=\"T_87ae0_row0_col3\" class=\"data row0 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row0_col5\" class=\"data row0 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row0_col6\" class=\"data row0 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ADF</td>\n",
-              "      <td id=\"T_87ae0_row1_col1\" class=\"data row1 col1\" >ADF</td>\n",
-              "      <td id=\"T_87ae0_row1_col2\" class=\"data row1 col2\" >Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....</td>\n",
-              "      <td id=\"T_87ae0_row1_col3\" class=\"data row1 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row1_col4\" class=\"data row1 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row1_col5\" class=\"data row1 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity']</td>\n",
-              "      <td id=\"T_87ae0_row1_col6\" class=\"data row1 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.AutoAR</td>\n",
-              "      <td id=\"T_87ae0_row2_col1\" class=\"data row2 col1\" >Auto AR</td>\n",
-              "      <td id=\"T_87ae0_row2_col2\" class=\"data row2 col2\" >Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....</td>\n",
-              "      <td id=\"T_87ae0_row2_col3\" class=\"data row2 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row2_col4\" class=\"data row2 col4\" >{'max_ar_order': {'type': 'int', 'default': 3}}</td>\n",
-              "      <td id=\"T_87ae0_row2_col5\" class=\"data row2 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
-              "      <td id=\"T_87ae0_row2_col6\" class=\"data row2 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.AutoMA</td>\n",
-              "      <td id=\"T_87ae0_row3_col1\" class=\"data row3 col1\" >Auto MA</td>\n",
-              "      <td id=\"T_87ae0_row3_col2\" class=\"data row3 col2\" >Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...</td>\n",
-              "      <td id=\"T_87ae0_row3_col3\" class=\"data row3 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row3_col4\" class=\"data row3 col4\" >{'max_ma_order': {'type': 'int', 'default': 3}}</td>\n",
-              "      <td id=\"T_87ae0_row3_col5\" class=\"data row3 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
-              "      <td id=\"T_87ae0_row3_col6\" class=\"data row3 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.AutoStationarity</td>\n",
-              "      <td id=\"T_87ae0_row4_col1\" class=\"data row4 col1\" >Auto Stationarity</td>\n",
-              "      <td id=\"T_87ae0_row4_col2\" class=\"data row4 col2\" >Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....</td>\n",
-              "      <td id=\"T_87ae0_row4_col3\" class=\"data row4 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row4_col4\" class=\"data row4 col4\" >{'max_order': {'type': 'int', 'default': 5}, 'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_87ae0_row4_col5\" class=\"data row4 col5\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
-              "      <td id=\"T_87ae0_row4_col6\" class=\"data row4 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
-              "      <td id=\"T_87ae0_row5_col1\" class=\"data row5 col1\" >Bivariate Scatter Plots</td>\n",
-              "      <td id=\"T_87ae0_row5_col2\" class=\"data row5 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
-              "      <td id=\"T_87ae0_row5_col3\" class=\"data row5 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row5_col5\" class=\"data row5 col5\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row5_col6\" class=\"data row5 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.BoxPierce</td>\n",
-              "      <td id=\"T_87ae0_row6_col1\" class=\"data row6 col1\" >Box Pierce</td>\n",
-              "      <td id=\"T_87ae0_row6_col2\" class=\"data row6 col2\" >Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....</td>\n",
-              "      <td id=\"T_87ae0_row6_col3\" class=\"data row6 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row6_col4\" class=\"data row6 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row6_col5\" class=\"data row6 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row6_col6\" class=\"data row6 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
-              "      <td id=\"T_87ae0_row7_col1\" class=\"data row7 col1\" >Chi Squared Features Table</td>\n",
-              "      <td id=\"T_87ae0_row7_col2\" class=\"data row7 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
-              "      <td id=\"T_87ae0_row7_col3\" class=\"data row7 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row7_col4\" class=\"data row7 col4\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_87ae0_row7_col5\" class=\"data row7 col5\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
-              "      <td id=\"T_87ae0_row7_col6\" class=\"data row7 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.ClassImbalance</td>\n",
-              "      <td id=\"T_87ae0_row8_col1\" class=\"data row8 col1\" >Class Imbalance</td>\n",
-              "      <td id=\"T_87ae0_row8_col2\" class=\"data row8 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
-              "      <td id=\"T_87ae0_row8_col3\" class=\"data row8 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row8_col4\" class=\"data row8 col4\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_87ae0_row8_col5\" class=\"data row8 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
-              "      <td id=\"T_87ae0_row8_col6\" class=\"data row8 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.DatasetDescription</td>\n",
-              "      <td id=\"T_87ae0_row9_col1\" class=\"data row9 col1\" >Dataset Description</td>\n",
-              "      <td id=\"T_87ae0_row9_col2\" class=\"data row9 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
-              "      <td id=\"T_87ae0_row9_col3\" class=\"data row9 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row9_col5\" class=\"data row9 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
-              "      <td id=\"T_87ae0_row9_col6\" class=\"data row9 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.DatasetSplit</td>\n",
-              "      <td id=\"T_87ae0_row10_col1\" class=\"data row10 col1\" >Dataset Split</td>\n",
-              "      <td id=\"T_87ae0_row10_col2\" class=\"data row10 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
-              "      <td id=\"T_87ae0_row10_col3\" class=\"data row10 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_87ae0_row10_col4\" class=\"data row10 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row10_col5\" class=\"data row10 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
-              "      <td id=\"T_87ae0_row10_col6\" class=\"data row10 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
-              "      <td id=\"T_87ae0_row11_col1\" class=\"data row11 col1\" >Descriptive Statistics</td>\n",
-              "      <td id=\"T_87ae0_row11_col2\" class=\"data row11 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
-              "      <td id=\"T_87ae0_row11_col3\" class=\"data row11 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row11_col4\" class=\"data row11 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row11_col5\" class=\"data row11 col5\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
-              "      <td id=\"T_87ae0_row11_col6\" class=\"data row11 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.DickeyFullerGLS</td>\n",
-              "      <td id=\"T_87ae0_row12_col1\" class=\"data row12 col1\" >Dickey Fuller GLS</td>\n",
-              "      <td id=\"T_87ae0_row12_col2\" class=\"data row12 col2\" >Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration....</td>\n",
-              "      <td id=\"T_87ae0_row12_col3\" class=\"data row12 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row12_col4\" class=\"data row12 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row12_col5\" class=\"data row12 col5\" >['time_series_data', 'forecasting', 'unit_root_test']</td>\n",
-              "      <td id=\"T_87ae0_row12_col6\" class=\"data row12 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.Duplicates</td>\n",
-              "      <td id=\"T_87ae0_row13_col1\" class=\"data row13 col1\" >Duplicates</td>\n",
-              "      <td id=\"T_87ae0_row13_col2\" class=\"data row13 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
-              "      <td id=\"T_87ae0_row13_col3\" class=\"data row13 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row13_col4\" class=\"data row13 col4\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
-              "      <td id=\"T_87ae0_row13_col5\" class=\"data row13 col5\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
-              "      <td id=\"T_87ae0_row13_col6\" class=\"data row13 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.EngleGrangerCoint</td>\n",
-              "      <td id=\"T_87ae0_row14_col1\" class=\"data row14 col1\" >Engle Granger Coint</td>\n",
-              "      <td id=\"T_87ae0_row14_col2\" class=\"data row14 col2\" >Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test....</td>\n",
-              "      <td id=\"T_87ae0_row14_col3\" class=\"data row14 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row14_col4\" class=\"data row14 col4\" >{'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_87ae0_row14_col5\" class=\"data row14 col5\" >['time_series_data', 'statistical_test', 'forecasting']</td>\n",
-              "      <td id=\"T_87ae0_row14_col6\" class=\"data row14 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
-              "      <td id=\"T_87ae0_row15_col1\" class=\"data row15 col1\" >Feature Target Correlation Plot</td>\n",
-              "      <td id=\"T_87ae0_row15_col2\" class=\"data row15 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
-              "      <td id=\"T_87ae0_row15_col3\" class=\"data row15 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row15_col4\" class=\"data row15 col4\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
-              "      <td id=\"T_87ae0_row15_col5\" class=\"data row15 col5\" >['tabular_data', 'visualization', 'correlation']</td>\n",
-              "      <td id=\"T_87ae0_row15_col6\" class=\"data row15 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.HighCardinality</td>\n",
-              "      <td id=\"T_87ae0_row16_col1\" class=\"data row16 col1\" >High Cardinality</td>\n",
-              "      <td id=\"T_87ae0_row16_col2\" class=\"data row16 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
-              "      <td id=\"T_87ae0_row16_col3\" class=\"data row16 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row16_col4\" class=\"data row16 col4\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
-              "      <td id=\"T_87ae0_row16_col5\" class=\"data row16 col5\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
-              "      <td id=\"T_87ae0_row16_col6\" class=\"data row16 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
-              "      <td id=\"T_87ae0_row17_col1\" class=\"data row17 col1\" >High Pearson Correlation</td>\n",
-              "      <td id=\"T_87ae0_row17_col2\" class=\"data row17 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
-              "      <td id=\"T_87ae0_row17_col3\" class=\"data row17 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row17_col4\" class=\"data row17 col4\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row17_col5\" class=\"data row17 col5\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
-              "      <td id=\"T_87ae0_row17_col6\" class=\"data row17 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
-              "      <td id=\"T_87ae0_row18_col1\" class=\"data row18 col1\" >IQR Outliers Bar Plot</td>\n",
-              "      <td id=\"T_87ae0_row18_col2\" class=\"data row18 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
-              "      <td id=\"T_87ae0_row18_col3\" class=\"data row18 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row18_col4\" class=\"data row18 col4\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
-              "      <td id=\"T_87ae0_row18_col5\" class=\"data row18 col5\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
-              "      <td id=\"T_87ae0_row18_col6\" class=\"data row18 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
-              "      <td id=\"T_87ae0_row19_col1\" class=\"data row19 col1\" >IQR Outliers Table</td>\n",
-              "      <td id=\"T_87ae0_row19_col2\" class=\"data row19 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
-              "      <td id=\"T_87ae0_row19_col3\" class=\"data row19 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row19_col4\" class=\"data row19 col4\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
-              "      <td id=\"T_87ae0_row19_col5\" class=\"data row19 col5\" >['tabular_data', 'numerical_data']</td>\n",
-              "      <td id=\"T_87ae0_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
-              "      <td id=\"T_87ae0_row20_col1\" class=\"data row20 col1\" >Isolation Forest Outliers</td>\n",
-              "      <td id=\"T_87ae0_row20_col2\" class=\"data row20 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
-              "      <td id=\"T_87ae0_row20_col3\" class=\"data row20 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row20_col4\" class=\"data row20 col4\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row20_col5\" class=\"data row20 col5\" >['tabular_data', 'anomaly_detection']</td>\n",
-              "      <td id=\"T_87ae0_row20_col6\" class=\"data row20 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.JarqueBera</td>\n",
-              "      <td id=\"T_87ae0_row21_col1\" class=\"data row21 col1\" >Jarque Bera</td>\n",
-              "      <td id=\"T_87ae0_row21_col2\" class=\"data row21 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
-              "      <td id=\"T_87ae0_row21_col3\" class=\"data row21 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row21_col4\" class=\"data row21 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row21_col5\" class=\"data row21 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row21_col6\" class=\"data row21 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.KPSS</td>\n",
-              "      <td id=\"T_87ae0_row22_col1\" class=\"data row22 col1\" >KPSS</td>\n",
-              "      <td id=\"T_87ae0_row22_col2\" class=\"data row22 col2\" >Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test....</td>\n",
-              "      <td id=\"T_87ae0_row22_col3\" class=\"data row22 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row22_col4\" class=\"data row22 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row22_col5\" class=\"data row22 col5\" >['time_series_data', 'stationarity', 'unit_root_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row22_col6\" class=\"data row22 col6\" >['data_validation']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.LJungBox</td>\n",
-              "      <td id=\"T_87ae0_row23_col1\" class=\"data row23 col1\" >L Jung Box</td>\n",
-              "      <td id=\"T_87ae0_row23_col2\" class=\"data row23 col2\" >Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....</td>\n",
-              "      <td id=\"T_87ae0_row23_col3\" class=\"data row23 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row23_col4\" class=\"data row23 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row23_col5\" class=\"data row23 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row23_col6\" class=\"data row23 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.LaggedCorrelationHeatmap</td>\n",
-              "      <td id=\"T_87ae0_row24_col1\" class=\"data row24 col1\" >Lagged Correlation Heatmap</td>\n",
-              "      <td id=\"T_87ae0_row24_col2\" class=\"data row24 col2\" >Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...</td>\n",
-              "      <td id=\"T_87ae0_row24_col3\" class=\"data row24 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row24_col4\" class=\"data row24 col4\" >{'num_lags': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_87ae0_row24_col5\" class=\"data row24 col5\" >['time_series_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row24_col6\" class=\"data row24 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.MissingValues</td>\n",
-              "      <td id=\"T_87ae0_row25_col1\" class=\"data row25 col1\" >Missing Values</td>\n",
-              "      <td id=\"T_87ae0_row25_col2\" class=\"data row25 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
-              "      <td id=\"T_87ae0_row25_col3\" class=\"data row25 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row25_col4\" class=\"data row25 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
-              "      <td id=\"T_87ae0_row25_col5\" class=\"data row25 col5\" >['tabular_data', 'data_quality']</td>\n",
-              "      <td id=\"T_87ae0_row25_col6\" class=\"data row25 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
-              "      <td id=\"T_87ae0_row26_col1\" class=\"data row26 col1\" >Missing Values Bar Plot</td>\n",
-              "      <td id=\"T_87ae0_row26_col2\" class=\"data row26 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
-              "      <td id=\"T_87ae0_row26_col3\" class=\"data row26 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row26_col4\" class=\"data row26 col4\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
-              "      <td id=\"T_87ae0_row26_col5\" class=\"data row26 col5\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row26_col6\" class=\"data row26 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.MutualInformation</td>\n",
-              "      <td id=\"T_87ae0_row27_col1\" class=\"data row27 col1\" >Mutual Information</td>\n",
-              "      <td id=\"T_87ae0_row27_col2\" class=\"data row27 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
-              "      <td id=\"T_87ae0_row27_col3\" class=\"data row27 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row27_col4\" class=\"data row27 col4\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
-              "      <td id=\"T_87ae0_row27_col5\" class=\"data row27 col5\" >['feature_selection', 'data_analysis']</td>\n",
-              "      <td id=\"T_87ae0_row27_col6\" class=\"data row27 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
-              "      <td id=\"T_87ae0_row28_col1\" class=\"data row28 col1\" >Pearson Correlation Matrix</td>\n",
-              "      <td id=\"T_87ae0_row28_col2\" class=\"data row28 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
-              "      <td id=\"T_87ae0_row28_col3\" class=\"data row28 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row28_col5\" class=\"data row28 col5\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
-              "      <td id=\"T_87ae0_row28_col6\" class=\"data row28 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.PhillipsPerronArch</td>\n",
-              "      <td id=\"T_87ae0_row29_col1\" class=\"data row29 col1\" >Phillips Perron Arch</td>\n",
-              "      <td id=\"T_87ae0_row29_col2\" class=\"data row29 col2\" >Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test....</td>\n",
-              "      <td id=\"T_87ae0_row29_col3\" class=\"data row29 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row29_col4\" class=\"data row29 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row29_col5\" class=\"data row29 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'unit_root_test']</td>\n",
-              "      <td id=\"T_87ae0_row29_col6\" class=\"data row29 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
-              "      <td id=\"T_87ae0_row30_col1\" class=\"data row30 col1\" >Protected Classes Description</td>\n",
-              "      <td id=\"T_87ae0_row30_col2\" class=\"data row30 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
-              "      <td id=\"T_87ae0_row30_col3\" class=\"data row30 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row30_col4\" class=\"data row30 col4\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row30_col5\" class=\"data row30 col5\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
-              "      <td id=\"T_87ae0_row30_col6\" class=\"data row30 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.RollingStatsPlot</td>\n",
-              "      <td id=\"T_87ae0_row31_col1\" class=\"data row31 col1\" >Rolling Stats Plot</td>\n",
-              "      <td id=\"T_87ae0_row31_col2\" class=\"data row31 col2\" >Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified...</td>\n",
-              "      <td id=\"T_87ae0_row31_col3\" class=\"data row31 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row31_col4\" class=\"data row31 col4\" >{'window_size': {'type': 'int', 'default': 12}}</td>\n",
-              "      <td id=\"T_87ae0_row31_col5\" class=\"data row31 col5\" >['time_series_data', 'visualization', 'stationarity']</td>\n",
-              "      <td id=\"T_87ae0_row31_col6\" class=\"data row31 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.RunsTest</td>\n",
-              "      <td id=\"T_87ae0_row32_col1\" class=\"data row32 col1\" >Runs Test</td>\n",
-              "      <td id=\"T_87ae0_row32_col2\" class=\"data row32 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
-              "      <td id=\"T_87ae0_row32_col3\" class=\"data row32 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row32_col4\" class=\"data row32 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row32_col5\" class=\"data row32 col5\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row32_col6\" class=\"data row32 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row33_col0\" class=\"data row33 col0\" >validmind.data_validation.ScatterPlot</td>\n",
-              "      <td id=\"T_87ae0_row33_col1\" class=\"data row33 col1\" >Scatter Plot</td>\n",
-              "      <td id=\"T_87ae0_row33_col2\" class=\"data row33 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
-              "      <td id=\"T_87ae0_row33_col3\" class=\"data row33 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row33_col4\" class=\"data row33 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row33_col5\" class=\"data row33 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row33_col6\" class=\"data row33 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row34_col0\" class=\"data row34 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
-              "      <td id=\"T_87ae0_row34_col1\" class=\"data row34 col1\" >Score Band Default Rates</td>\n",
-              "      <td id=\"T_87ae0_row34_col2\" class=\"data row34 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
-              "      <td id=\"T_87ae0_row34_col3\" class=\"data row34 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row34_col4\" class=\"data row34 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row34_col5\" class=\"data row34 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
-              "      <td id=\"T_87ae0_row34_col6\" class=\"data row34 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row35_col0\" class=\"data row35 col0\" >validmind.data_validation.SeasonalDecompose</td>\n",
-              "      <td id=\"T_87ae0_row35_col1\" class=\"data row35 col1\" >Seasonal Decompose</td>\n",
-              "      <td id=\"T_87ae0_row35_col2\" class=\"data row35 col2\" >Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components....</td>\n",
-              "      <td id=\"T_87ae0_row35_col3\" class=\"data row35 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row35_col4\" class=\"data row35 col4\" >{'seasonal_model': {'type': 'str', 'default': 'additive'}}</td>\n",
-              "      <td id=\"T_87ae0_row35_col5\" class=\"data row35 col5\" >['time_series_data', 'seasonality', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row35_col6\" class=\"data row35 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row36_col0\" class=\"data row36 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
-              "      <td id=\"T_87ae0_row36_col1\" class=\"data row36 col1\" >Shapiro Wilk</td>\n",
-              "      <td id=\"T_87ae0_row36_col2\" class=\"data row36 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
-              "      <td id=\"T_87ae0_row36_col3\" class=\"data row36 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row36_col4\" class=\"data row36 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row36_col5\" class=\"data row36 col5\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
-              "      <td id=\"T_87ae0_row36_col6\" class=\"data row36 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row37_col0\" class=\"data row37 col0\" >validmind.data_validation.Skewness</td>\n",
-              "      <td id=\"T_87ae0_row37_col1\" class=\"data row37 col1\" >Skewness</td>\n",
-              "      <td id=\"T_87ae0_row37_col2\" class=\"data row37 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
-              "      <td id=\"T_87ae0_row37_col3\" class=\"data row37 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row37_col4\" class=\"data row37 col4\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
-              "      <td id=\"T_87ae0_row37_col5\" class=\"data row37 col5\" >['data_quality', 'tabular_data']</td>\n",
-              "      <td id=\"T_87ae0_row37_col6\" class=\"data row37 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row38_col0\" class=\"data row38 col0\" >validmind.data_validation.SpreadPlot</td>\n",
-              "      <td id=\"T_87ae0_row38_col1\" class=\"data row38 col1\" >Spread Plot</td>\n",
-              "      <td id=\"T_87ae0_row38_col2\" class=\"data row38 col2\" >Assesses potential correlations between pairs of time series variables through visualization to enhance...</td>\n",
-              "      <td id=\"T_87ae0_row38_col3\" class=\"data row38 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row38_col4\" class=\"data row38 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row38_col5\" class=\"data row38 col5\" >['time_series_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row38_col6\" class=\"data row38 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row39_col0\" class=\"data row39 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
-              "      <td id=\"T_87ae0_row39_col1\" class=\"data row39 col1\" >Tabular Categorical Bar Plots</td>\n",
-              "      <td id=\"T_87ae0_row39_col2\" class=\"data row39 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
-              "      <td id=\"T_87ae0_row39_col3\" class=\"data row39 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row39_col4\" class=\"data row39 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row39_col5\" class=\"data row39 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row39_col6\" class=\"data row39 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row40_col0\" class=\"data row40 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
-              "      <td id=\"T_87ae0_row40_col1\" class=\"data row40 col1\" >Tabular Date Time Histograms</td>\n",
-              "      <td id=\"T_87ae0_row40_col2\" class=\"data row40 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
-              "      <td id=\"T_87ae0_row40_col3\" class=\"data row40 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row40_col4\" class=\"data row40 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row40_col5\" class=\"data row40 col5\" >['time_series_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row40_col6\" class=\"data row40 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row41_col0\" class=\"data row41 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
-              "      <td id=\"T_87ae0_row41_col1\" class=\"data row41 col1\" >Tabular Description Tables</td>\n",
-              "      <td id=\"T_87ae0_row41_col2\" class=\"data row41 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
-              "      <td id=\"T_87ae0_row41_col3\" class=\"data row41 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row41_col4\" class=\"data row41 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row41_col5\" class=\"data row41 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_87ae0_row41_col6\" class=\"data row41 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row42_col0\" class=\"data row42 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
-              "      <td id=\"T_87ae0_row42_col1\" class=\"data row42 col1\" >Tabular Numerical Histograms</td>\n",
-              "      <td id=\"T_87ae0_row42_col2\" class=\"data row42 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
-              "      <td id=\"T_87ae0_row42_col3\" class=\"data row42 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row42_col4\" class=\"data row42 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row42_col5\" class=\"data row42 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row42_col6\" class=\"data row42 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row43_col0\" class=\"data row43 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
-              "      <td id=\"T_87ae0_row43_col1\" class=\"data row43 col1\" >Target Rate Bar Plots</td>\n",
-              "      <td id=\"T_87ae0_row43_col2\" class=\"data row43 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
-              "      <td id=\"T_87ae0_row43_col3\" class=\"data row43 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row43_col4\" class=\"data row43 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row43_col5\" class=\"data row43 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
-              "      <td id=\"T_87ae0_row43_col6\" class=\"data row43 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row44_col0\" class=\"data row44 col0\" >validmind.data_validation.TimeSeriesDescription</td>\n",
-              "      <td id=\"T_87ae0_row44_col1\" class=\"data row44 col1\" >Time Series Description</td>\n",
-              "      <td id=\"T_87ae0_row44_col2\" class=\"data row44 col2\" >Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,...</td>\n",
-              "      <td id=\"T_87ae0_row44_col3\" class=\"data row44 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row44_col4\" class=\"data row44 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row44_col5\" class=\"data row44 col5\" >['time_series_data', 'analysis']</td>\n",
-              "      <td id=\"T_87ae0_row44_col6\" class=\"data row44 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row45_col0\" class=\"data row45 col0\" >validmind.data_validation.TimeSeriesDescriptiveStatistics</td>\n",
-              "      <td id=\"T_87ae0_row45_col1\" class=\"data row45 col1\" >Time Series Descriptive Statistics</td>\n",
-              "      <td id=\"T_87ae0_row45_col2\" class=\"data row45 col2\" >Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues....</td>\n",
-              "      <td id=\"T_87ae0_row45_col3\" class=\"data row45 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row45_col4\" class=\"data row45 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row45_col5\" class=\"data row45 col5\" >['time_series_data', 'analysis']</td>\n",
-              "      <td id=\"T_87ae0_row45_col6\" class=\"data row45 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row46_col0\" class=\"data row46 col0\" >validmind.data_validation.TimeSeriesFrequency</td>\n",
-              "      <td id=\"T_87ae0_row46_col1\" class=\"data row46 col1\" >Time Series Frequency</td>\n",
-              "      <td id=\"T_87ae0_row46_col2\" class=\"data row46 col2\" >Evaluates consistency of time series data frequency and generates a frequency plot....</td>\n",
-              "      <td id=\"T_87ae0_row46_col3\" class=\"data row46 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row46_col4\" class=\"data row46 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row46_col5\" class=\"data row46 col5\" >['time_series_data']</td>\n",
-              "      <td id=\"T_87ae0_row46_col6\" class=\"data row46 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row47_col0\" class=\"data row47 col0\" >validmind.data_validation.TimeSeriesHistogram</td>\n",
-              "      <td id=\"T_87ae0_row47_col1\" class=\"data row47 col1\" >Time Series Histogram</td>\n",
-              "      <td id=\"T_87ae0_row47_col2\" class=\"data row47 col2\" >Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....</td>\n",
-              "      <td id=\"T_87ae0_row47_col3\" class=\"data row47 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row47_col4\" class=\"data row47 col4\" >{'nbins': {'type': '_empty', 'default': 30}}</td>\n",
-              "      <td id=\"T_87ae0_row47_col5\" class=\"data row47 col5\" >['data_validation', 'visualization', 'time_series_data']</td>\n",
-              "      <td id=\"T_87ae0_row47_col6\" class=\"data row47 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row48_col0\" class=\"data row48 col0\" >validmind.data_validation.TimeSeriesLinePlot</td>\n",
-              "      <td id=\"T_87ae0_row48_col1\" class=\"data row48 col1\" >Time Series Line Plot</td>\n",
-              "      <td id=\"T_87ae0_row48_col2\" class=\"data row48 col2\" >Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....</td>\n",
-              "      <td id=\"T_87ae0_row48_col3\" class=\"data row48 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row48_col4\" class=\"data row48 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row48_col5\" class=\"data row48 col5\" >['time_series_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row48_col6\" class=\"data row48 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row49_col0\" class=\"data row49 col0\" >validmind.data_validation.TimeSeriesMissingValues</td>\n",
-              "      <td id=\"T_87ae0_row49_col1\" class=\"data row49 col1\" >Time Series Missing Values</td>\n",
-              "      <td id=\"T_87ae0_row49_col2\" class=\"data row49 col2\" >Validates time-series data quality by confirming the count of missing values is below a certain threshold....</td>\n",
-              "      <td id=\"T_87ae0_row49_col3\" class=\"data row49 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row49_col4\" class=\"data row49 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
-              "      <td id=\"T_87ae0_row49_col5\" class=\"data row49 col5\" >['time_series_data']</td>\n",
-              "      <td id=\"T_87ae0_row49_col6\" class=\"data row49 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row50_col0\" class=\"data row50 col0\" >validmind.data_validation.TimeSeriesOutliers</td>\n",
-              "      <td id=\"T_87ae0_row50_col1\" class=\"data row50 col1\" >Time Series Outliers</td>\n",
-              "      <td id=\"T_87ae0_row50_col2\" class=\"data row50 col2\" >Identifies and visualizes outliers in time-series data using the z-score method....</td>\n",
-              "      <td id=\"T_87ae0_row50_col3\" class=\"data row50 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row50_col4\" class=\"data row50 col4\" >{'zscore_threshold': {'type': 'int', 'default': 3}}</td>\n",
-              "      <td id=\"T_87ae0_row50_col5\" class=\"data row50 col5\" >['time_series_data']</td>\n",
-              "      <td id=\"T_87ae0_row50_col6\" class=\"data row50 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row51_col0\" class=\"data row51 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
-              "      <td id=\"T_87ae0_row51_col1\" class=\"data row51 col1\" >Too Many Zero Values</td>\n",
-              "      <td id=\"T_87ae0_row51_col2\" class=\"data row51 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
-              "      <td id=\"T_87ae0_row51_col3\" class=\"data row51 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row51_col4\" class=\"data row51 col4\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
-              "      <td id=\"T_87ae0_row51_col5\" class=\"data row51 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_87ae0_row51_col6\" class=\"data row51 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row52_col0\" class=\"data row52 col0\" >validmind.data_validation.UniqueRows</td>\n",
-              "      <td id=\"T_87ae0_row52_col1\" class=\"data row52 col1\" >Unique Rows</td>\n",
-              "      <td id=\"T_87ae0_row52_col2\" class=\"data row52 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
-              "      <td id=\"T_87ae0_row52_col3\" class=\"data row52 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row52_col4\" class=\"data row52 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
-              "      <td id=\"T_87ae0_row52_col5\" class=\"data row52 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_87ae0_row52_col6\" class=\"data row52 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row53_col0\" class=\"data row53 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
-              "      <td id=\"T_87ae0_row53_col1\" class=\"data row53 col1\" >WOE Bin Plots</td>\n",
-              "      <td id=\"T_87ae0_row53_col2\" class=\"data row53 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
-              "      <td id=\"T_87ae0_row53_col3\" class=\"data row53 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row53_col4\" class=\"data row53 col4\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
-              "      <td id=\"T_87ae0_row53_col5\" class=\"data row53 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
-              "      <td id=\"T_87ae0_row53_col6\" class=\"data row53 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row54_col0\" class=\"data row54 col0\" >validmind.data_validation.WOEBinTable</td>\n",
-              "      <td id=\"T_87ae0_row54_col1\" class=\"data row54 col1\" >WOE Bin Table</td>\n",
-              "      <td id=\"T_87ae0_row54_col2\" class=\"data row54 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
-              "      <td id=\"T_87ae0_row54_col3\" class=\"data row54 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row54_col4\" class=\"data row54 col4\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row54_col5\" class=\"data row54 col5\" >['tabular_data', 'categorical_data']</td>\n",
-              "      <td id=\"T_87ae0_row54_col6\" class=\"data row54 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row55_col0\" class=\"data row55 col0\" >validmind.data_validation.ZivotAndrewsArch</td>\n",
-              "      <td id=\"T_87ae0_row55_col1\" class=\"data row55 col1\" >Zivot Andrews Arch</td>\n",
-              "      <td id=\"T_87ae0_row55_col2\" class=\"data row55 col2\" >Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test....</td>\n",
-              "      <td id=\"T_87ae0_row55_col3\" class=\"data row55 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row55_col4\" class=\"data row55 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row55_col5\" class=\"data row55 col5\" >['time_series_data', 'stationarity', 'unit_root_test']</td>\n",
-              "      <td id=\"T_87ae0_row55_col6\" class=\"data row55 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row56_col0\" class=\"data row56 col0\" >validmind.data_validation.nlp.CommonWords</td>\n",
-              "      <td id=\"T_87ae0_row56_col1\" class=\"data row56 col1\" >Common Words</td>\n",
-              "      <td id=\"T_87ae0_row56_col2\" class=\"data row56 col2\" >Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns....</td>\n",
-              "      <td id=\"T_87ae0_row56_col3\" class=\"data row56 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row56_col4\" class=\"data row56 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row56_col5\" class=\"data row56 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
-              "      <td id=\"T_87ae0_row56_col6\" class=\"data row56 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row57_col0\" class=\"data row57 col0\" >validmind.data_validation.nlp.Hashtags</td>\n",
-              "      <td id=\"T_87ae0_row57_col1\" class=\"data row57 col1\" >Hashtags</td>\n",
-              "      <td id=\"T_87ae0_row57_col2\" class=\"data row57 col2\" >Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....</td>\n",
-              "      <td id=\"T_87ae0_row57_col3\" class=\"data row57 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row57_col4\" class=\"data row57 col4\" >{'top_hashtags': {'type': 'int', 'default': 25}}</td>\n",
-              "      <td id=\"T_87ae0_row57_col5\" class=\"data row57 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
-              "      <td id=\"T_87ae0_row57_col6\" class=\"data row57 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row58_col0\" class=\"data row58 col0\" >validmind.data_validation.nlp.LanguageDetection</td>\n",
-              "      <td id=\"T_87ae0_row58_col1\" class=\"data row58 col1\" >Language Detection</td>\n",
-              "      <td id=\"T_87ae0_row58_col2\" class=\"data row58 col2\" >Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages....</td>\n",
-              "      <td id=\"T_87ae0_row58_col3\" class=\"data row58 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row58_col4\" class=\"data row58 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row58_col5\" class=\"data row58 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row58_col6\" class=\"data row58 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row59_col0\" class=\"data row59 col0\" >validmind.data_validation.nlp.Mentions</td>\n",
-              "      <td id=\"T_87ae0_row59_col1\" class=\"data row59 col1\" >Mentions</td>\n",
-              "      <td id=\"T_87ae0_row59_col2\" class=\"data row59 col2\" >Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....</td>\n",
-              "      <td id=\"T_87ae0_row59_col3\" class=\"data row59 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row59_col4\" class=\"data row59 col4\" >{'top_mentions': {'type': 'int', 'default': 25}}</td>\n",
-              "      <td id=\"T_87ae0_row59_col5\" class=\"data row59 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
-              "      <td id=\"T_87ae0_row59_col6\" class=\"data row59 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row60_col0\" class=\"data row60 col0\" >validmind.data_validation.nlp.PolarityAndSubjectivity</td>\n",
-              "      <td id=\"T_87ae0_row60_col1\" class=\"data row60 col1\" >Polarity And Subjectivity</td>\n",
-              "      <td id=\"T_87ae0_row60_col2\" class=\"data row60 col2\" >Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution....</td>\n",
-              "      <td id=\"T_87ae0_row60_col3\" class=\"data row60 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row60_col4\" class=\"data row60 col4\" >{'threshold_subjectivity': {'type': '_empty', 'default': 0.5}, 'threshold_polarity': {'type': '_empty', 'default': 0}}</td>\n",
-              "      <td id=\"T_87ae0_row60_col5\" class=\"data row60 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
-              "      <td id=\"T_87ae0_row60_col6\" class=\"data row60 col6\" >['nlp']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row61_col0\" class=\"data row61 col0\" >validmind.data_validation.nlp.Punctuations</td>\n",
-              "      <td id=\"T_87ae0_row61_col1\" class=\"data row61 col1\" >Punctuations</td>\n",
-              "      <td id=\"T_87ae0_row61_col2\" class=\"data row61 col2\" >Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....</td>\n",
-              "      <td id=\"T_87ae0_row61_col3\" class=\"data row61 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row61_col4\" class=\"data row61 col4\" >{'count_mode': {'type': '_empty', 'default': 'token'}}</td>\n",
-              "      <td id=\"T_87ae0_row61_col5\" class=\"data row61 col5\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
-              "      <td id=\"T_87ae0_row61_col6\" class=\"data row61 col6\" >['text_classification', 'text_summarization', 'nlp']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row62_col0\" class=\"data row62 col0\" >validmind.data_validation.nlp.Sentiment</td>\n",
-              "      <td id=\"T_87ae0_row62_col1\" class=\"data row62 col1\" >Sentiment</td>\n",
-              "      <td id=\"T_87ae0_row62_col2\" class=\"data row62 col2\" >Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....</td>\n",
-              "      <td id=\"T_87ae0_row62_col3\" class=\"data row62 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row62_col4\" class=\"data row62 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row62_col5\" class=\"data row62 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
-              "      <td id=\"T_87ae0_row62_col6\" class=\"data row62 col6\" >['nlp']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row63_col0\" class=\"data row63 col0\" >validmind.data_validation.nlp.StopWords</td>\n",
-              "      <td id=\"T_87ae0_row63_col1\" class=\"data row63 col1\" >Stop Words</td>\n",
-              "      <td id=\"T_87ae0_row63_col2\" class=\"data row63 col2\" >Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....</td>\n",
-              "      <td id=\"T_87ae0_row63_col3\" class=\"data row63 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row63_col4\" class=\"data row63 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 0.5}, 'num_words': {'type': 'int', 'default': 25}}</td>\n",
-              "      <td id=\"T_87ae0_row63_col5\" class=\"data row63 col5\" >['nlp', 'text_data', 'frequency_analysis', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row63_col6\" class=\"data row63 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row64_col0\" class=\"data row64 col0\" >validmind.data_validation.nlp.TextDescription</td>\n",
-              "      <td id=\"T_87ae0_row64_col1\" class=\"data row64 col1\" >Text Description</td>\n",
-              "      <td id=\"T_87ae0_row64_col2\" class=\"data row64 col2\" >Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate...</td>\n",
-              "      <td id=\"T_87ae0_row64_col3\" class=\"data row64 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row64_col4\" class=\"data row64 col4\" >{'unwanted_tokens': {'type': 'set', 'default': {\"s'\", \"'s\", ' ', 'mr', \"''\", 'dollar', 'dr', 'mrs', '``', 's', 'us', 'ms'}}, 'lang': {'type': 'str', 'default': 'english'}}</td>\n",
-              "      <td id=\"T_87ae0_row64_col5\" class=\"data row64 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row64_col6\" class=\"data row64 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row65_col0\" class=\"data row65 col0\" >validmind.data_validation.nlp.Toxicity</td>\n",
-              "      <td id=\"T_87ae0_row65_col1\" class=\"data row65 col1\" >Toxicity</td>\n",
-              "      <td id=\"T_87ae0_row65_col2\" class=\"data row65 col2\" >Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores....</td>\n",
-              "      <td id=\"T_87ae0_row65_col3\" class=\"data row65 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row65_col4\" class=\"data row65 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row65_col5\" class=\"data row65 col5\" >['nlp', 'text_data', 'data_validation']</td>\n",
-              "      <td id=\"T_87ae0_row65_col6\" class=\"data row65 col6\" >['nlp']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row66_col0\" class=\"data row66 col0\" >validmind.model_validation.BertScore</td>\n",
-              "      <td id=\"T_87ae0_row66_col1\" class=\"data row66 col1\" >Bert Score</td>\n",
-              "      <td id=\"T_87ae0_row66_col2\" class=\"data row66 col2\" >Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms...</td>\n",
-              "      <td id=\"T_87ae0_row66_col3\" class=\"data row66 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row66_col4\" class=\"data row66 col4\" >{'evaluation_model': {'type': '_empty', 'default': 'distilbert-base-uncased'}}</td>\n",
-              "      <td id=\"T_87ae0_row66_col5\" class=\"data row66 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row66_col6\" class=\"data row66 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row67_col0\" class=\"data row67 col0\" >validmind.model_validation.BleuScore</td>\n",
-              "      <td id=\"T_87ae0_row67_col1\" class=\"data row67 col1\" >Bleu Score</td>\n",
-              "      <td id=\"T_87ae0_row67_col2\" class=\"data row67 col2\" >Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...</td>\n",
-              "      <td id=\"T_87ae0_row67_col3\" class=\"data row67 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row67_col4\" class=\"data row67 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row67_col5\" class=\"data row67 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row67_col6\" class=\"data row67 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row68_col0\" class=\"data row68 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
-              "      <td id=\"T_87ae0_row68_col1\" class=\"data row68 col1\" >Cluster Size Distribution</td>\n",
-              "      <td id=\"T_87ae0_row68_col2\" class=\"data row68 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
-              "      <td id=\"T_87ae0_row68_col3\" class=\"data row68 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row68_col4\" class=\"data row68 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row68_col5\" class=\"data row68 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row68_col6\" class=\"data row68 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row69_col0\" class=\"data row69 col0\" >validmind.model_validation.ContextualRecall</td>\n",
-              "      <td id=\"T_87ae0_row69_col1\" class=\"data row69 col1\" >Contextual Recall</td>\n",
-              "      <td id=\"T_87ae0_row69_col2\" class=\"data row69 col2\" >Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct...</td>\n",
-              "      <td id=\"T_87ae0_row69_col3\" class=\"data row69 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row69_col4\" class=\"data row69 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row69_col5\" class=\"data row69 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row69_col6\" class=\"data row69 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row70_col0\" class=\"data row70 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
-              "      <td id=\"T_87ae0_row70_col1\" class=\"data row70 col1\" >Features AUC</td>\n",
-              "      <td id=\"T_87ae0_row70_col2\" class=\"data row70 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
-              "      <td id=\"T_87ae0_row70_col3\" class=\"data row70 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row70_col4\" class=\"data row70 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
-              "      <td id=\"T_87ae0_row70_col5\" class=\"data row70 col5\" >['feature_importance', 'AUC', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row70_col6\" class=\"data row70 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row71_col0\" class=\"data row71 col0\" >validmind.model_validation.MeteorScore</td>\n",
-              "      <td id=\"T_87ae0_row71_col1\" class=\"data row71 col1\" >Meteor Score</td>\n",
-              "      <td id=\"T_87ae0_row71_col2\" class=\"data row71 col2\" >Assesses the quality of machine-generated translations by comparing them to human-produced references using the...</td>\n",
-              "      <td id=\"T_87ae0_row71_col3\" class=\"data row71 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row71_col4\" class=\"data row71 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row71_col5\" class=\"data row71 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row71_col6\" class=\"data row71 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row72_col0\" class=\"data row72 col0\" >validmind.model_validation.ModelMetadata</td>\n",
-              "      <td id=\"T_87ae0_row72_col1\" class=\"data row72 col1\" >Model Metadata</td>\n",
-              "      <td id=\"T_87ae0_row72_col2\" class=\"data row72 col2\" >Compare metadata of different models and generate a summary table with the results....</td>\n",
-              "      <td id=\"T_87ae0_row72_col3\" class=\"data row72 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row72_col4\" class=\"data row72 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row72_col5\" class=\"data row72 col5\" >['model_training', 'metadata']</td>\n",
-              "      <td id=\"T_87ae0_row72_col6\" class=\"data row72 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row73_col0\" class=\"data row73 col0\" >validmind.model_validation.ModelPredictionResiduals</td>\n",
-              "      <td id=\"T_87ae0_row73_col1\" class=\"data row73 col1\" >Model Prediction Residuals</td>\n",
-              "      <td id=\"T_87ae0_row73_col2\" class=\"data row73 col2\" >Assesses normality and behavior of residuals in regression models through visualization and statistical tests....</td>\n",
-              "      <td id=\"T_87ae0_row73_col3\" class=\"data row73 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row73_col4\" class=\"data row73 col4\" >{'nbins': {'type': '_empty', 'default': 100}, 'p_value_threshold': {'type': '_empty', 'default': 0.05}, 'start_date': {'type': '_empty', 'default': None}, 'end_date': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row73_col5\" class=\"data row73 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row73_col6\" class=\"data row73 col6\" >['residual_analysis', 'visualization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row74_col0\" class=\"data row74 col0\" >validmind.model_validation.RegardScore</td>\n",
-              "      <td id=\"T_87ae0_row74_col1\" class=\"data row74 col1\" >Regard Score</td>\n",
-              "      <td id=\"T_87ae0_row74_col2\" class=\"data row74 col2\" >Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard...</td>\n",
-              "      <td id=\"T_87ae0_row74_col3\" class=\"data row74 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row74_col4\" class=\"data row74 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row74_col5\" class=\"data row74 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row74_col6\" class=\"data row74 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row75_col0\" class=\"data row75 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
-              "      <td id=\"T_87ae0_row75_col1\" class=\"data row75 col1\" >Regression Residuals Plot</td>\n",
-              "      <td id=\"T_87ae0_row75_col2\" class=\"data row75 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
-              "      <td id=\"T_87ae0_row75_col3\" class=\"data row75 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row75_col4\" class=\"data row75 col4\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_87ae0_row75_col5\" class=\"data row75 col5\" >['model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row75_col6\" class=\"data row75 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row76_col0\" class=\"data row76 col0\" >validmind.model_validation.RougeScore</td>\n",
-              "      <td id=\"T_87ae0_row76_col1\" class=\"data row76 col1\" >Rouge Score</td>\n",
-              "      <td id=\"T_87ae0_row76_col2\" class=\"data row76 col2\" >Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide...</td>\n",
-              "      <td id=\"T_87ae0_row76_col3\" class=\"data row76 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row76_col4\" class=\"data row76 col4\" >{'metric': {'type': '_empty', 'default': 'rouge-1'}}</td>\n",
-              "      <td id=\"T_87ae0_row76_col5\" class=\"data row76 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row76_col6\" class=\"data row76 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row77_col0\" class=\"data row77 col0\" >validmind.model_validation.TimeSeriesPredictionWithCI</td>\n",
-              "      <td id=\"T_87ae0_row77_col1\" class=\"data row77 col1\" >Time Series Prediction With CI</td>\n",
-              "      <td id=\"T_87ae0_row77_col2\" class=\"data row77 col2\" >Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence...</td>\n",
-              "      <td id=\"T_87ae0_row77_col3\" class=\"data row77 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row77_col4\" class=\"data row77 col4\" >{'confidence': {'type': '_empty', 'default': 0.95}}</td>\n",
-              "      <td id=\"T_87ae0_row77_col5\" class=\"data row77 col5\" >['model_predictions', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row77_col6\" class=\"data row77 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row78_col0\" class=\"data row78 col0\" >validmind.model_validation.TimeSeriesPredictionsPlot</td>\n",
-              "      <td id=\"T_87ae0_row78_col1\" class=\"data row78 col1\" >Time Series Predictions Plot</td>\n",
-              "      <td id=\"T_87ae0_row78_col2\" class=\"data row78 col2\" >Plot actual vs predicted values for time series data and generate a visual comparison for the model....</td>\n",
-              "      <td id=\"T_87ae0_row78_col3\" class=\"data row78 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row78_col4\" class=\"data row78 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row78_col5\" class=\"data row78 col5\" >['model_predictions', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row78_col6\" class=\"data row78 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row79_col0\" class=\"data row79 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
-              "      <td id=\"T_87ae0_row79_col1\" class=\"data row79 col1\" >Time Series R2 Square By Segments</td>\n",
-              "      <td id=\"T_87ae0_row79_col2\" class=\"data row79 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
-              "      <td id=\"T_87ae0_row79_col3\" class=\"data row79 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row79_col4\" class=\"data row79 col4\" >{'segments': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row79_col5\" class=\"data row79 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_87ae0_row79_col6\" class=\"data row79 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row80_col0\" class=\"data row80 col0\" >validmind.model_validation.TokenDisparity</td>\n",
-              "      <td id=\"T_87ae0_row80_col1\" class=\"data row80 col1\" >Token Disparity</td>\n",
-              "      <td id=\"T_87ae0_row80_col2\" class=\"data row80 col2\" >Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and...</td>\n",
-              "      <td id=\"T_87ae0_row80_col3\" class=\"data row80 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row80_col4\" class=\"data row80 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row80_col5\" class=\"data row80 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row80_col6\" class=\"data row80 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row81_col0\" class=\"data row81 col0\" >validmind.model_validation.ToxicityScore</td>\n",
-              "      <td id=\"T_87ae0_row81_col1\" class=\"data row81 col1\" >Toxicity Score</td>\n",
-              "      <td id=\"T_87ae0_row81_col2\" class=\"data row81 col2\" >Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content....</td>\n",
-              "      <td id=\"T_87ae0_row81_col3\" class=\"data row81 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row81_col4\" class=\"data row81 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row81_col5\" class=\"data row81 col5\" >['nlp', 'text_data', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row81_col6\" class=\"data row81 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row82_col0\" class=\"data row82 col0\" >validmind.model_validation.embeddings.ClusterDistribution</td>\n",
-              "      <td id=\"T_87ae0_row82_col1\" class=\"data row82 col1\" >Cluster Distribution</td>\n",
-              "      <td id=\"T_87ae0_row82_col2\" class=\"data row82 col2\" >Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....</td>\n",
-              "      <td id=\"T_87ae0_row82_col3\" class=\"data row82 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row82_col4\" class=\"data row82 col4\" >{'num_clusters': {'type': 'int', 'default': 5}}</td>\n",
-              "      <td id=\"T_87ae0_row82_col5\" class=\"data row82 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row82_col6\" class=\"data row82 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row83_col0\" class=\"data row83 col0\" >validmind.model_validation.embeddings.CosineSimilarityComparison</td>\n",
-              "      <td id=\"T_87ae0_row83_col1\" class=\"data row83 col1\" >Cosine Similarity Comparison</td>\n",
-              "      <td id=\"T_87ae0_row83_col2\" class=\"data row83 col2\" >Assesses the similarity between embeddings generated by different models using Cosine Similarity, providing both...</td>\n",
-              "      <td id=\"T_87ae0_row83_col3\" class=\"data row83 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_87ae0_row83_col4\" class=\"data row83 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row83_col5\" class=\"data row83 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row83_col6\" class=\"data row83 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row84_col0\" class=\"data row84 col0\" >validmind.model_validation.embeddings.CosineSimilarityDistribution</td>\n",
-              "      <td id=\"T_87ae0_row84_col1\" class=\"data row84 col1\" >Cosine Similarity Distribution</td>\n",
-              "      <td id=\"T_87ae0_row84_col2\" class=\"data row84 col2\" >Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...</td>\n",
-              "      <td id=\"T_87ae0_row84_col3\" class=\"data row84 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row84_col4\" class=\"data row84 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row84_col5\" class=\"data row84 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row84_col6\" class=\"data row84 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row85_col0\" class=\"data row85 col0\" >validmind.model_validation.embeddings.CosineSimilarityHeatmap</td>\n",
-              "      <td id=\"T_87ae0_row85_col1\" class=\"data row85 col1\" >Cosine Similarity Heatmap</td>\n",
-              "      <td id=\"T_87ae0_row85_col2\" class=\"data row85 col2\" >Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....</td>\n",
-              "      <td id=\"T_87ae0_row85_col3\" class=\"data row85 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row85_col4\" class=\"data row85 col4\" >{'title': {'type': '_empty', 'default': 'Cosine Similarity Matrix'}, 'color': {'type': '_empty', 'default': 'Cosine Similarity'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
-              "      <td id=\"T_87ae0_row85_col5\" class=\"data row85 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row85_col6\" class=\"data row85 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row86_col0\" class=\"data row86 col0\" >validmind.model_validation.embeddings.DescriptiveAnalytics</td>\n",
-              "      <td id=\"T_87ae0_row86_col1\" class=\"data row86 col1\" >Descriptive Analytics</td>\n",
-              "      <td id=\"T_87ae0_row86_col2\" class=\"data row86 col2\" >Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...</td>\n",
-              "      <td id=\"T_87ae0_row86_col3\" class=\"data row86 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row86_col4\" class=\"data row86 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row86_col5\" class=\"data row86 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row86_col6\" class=\"data row86 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row87_col0\" class=\"data row87 col0\" >validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
-              "      <td id=\"T_87ae0_row87_col1\" class=\"data row87 col1\" >Embeddings Visualization2 D</td>\n",
-              "      <td id=\"T_87ae0_row87_col2\" class=\"data row87 col2\" >Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....</td>\n",
-              "      <td id=\"T_87ae0_row87_col3\" class=\"data row87 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row87_col4\" class=\"data row87 col4\" >{'cluster_column': {'type': None, 'default': None}, 'perplexity': {'type': 'int', 'default': 30}}</td>\n",
-              "      <td id=\"T_87ae0_row87_col5\" class=\"data row87 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row87_col6\" class=\"data row87 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row88_col0\" class=\"data row88 col0\" >validmind.model_validation.embeddings.EuclideanDistanceComparison</td>\n",
-              "      <td id=\"T_87ae0_row88_col1\" class=\"data row88 col1\" >Euclidean Distance Comparison</td>\n",
-              "      <td id=\"T_87ae0_row88_col2\" class=\"data row88 col2\" >Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights...</td>\n",
-              "      <td id=\"T_87ae0_row88_col3\" class=\"data row88 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_87ae0_row88_col4\" class=\"data row88 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row88_col5\" class=\"data row88 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row88_col6\" class=\"data row88 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row89_col0\" class=\"data row89 col0\" >validmind.model_validation.embeddings.EuclideanDistanceHeatmap</td>\n",
-              "      <td id=\"T_87ae0_row89_col1\" class=\"data row89 col1\" >Euclidean Distance Heatmap</td>\n",
-              "      <td id=\"T_87ae0_row89_col2\" class=\"data row89 col2\" >Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....</td>\n",
-              "      <td id=\"T_87ae0_row89_col3\" class=\"data row89 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row89_col4\" class=\"data row89 col4\" >{'title': {'type': '_empty', 'default': 'Euclidean Distance Matrix'}, 'color': {'type': '_empty', 'default': 'Euclidean Distance'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
-              "      <td id=\"T_87ae0_row89_col5\" class=\"data row89 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row89_col6\" class=\"data row89 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row90_col0\" class=\"data row90 col0\" >validmind.model_validation.embeddings.PCAComponentsPairwisePlots</td>\n",
-              "      <td id=\"T_87ae0_row90_col1\" class=\"data row90 col1\" >PCA Components Pairwise Plots</td>\n",
-              "      <td id=\"T_87ae0_row90_col2\" class=\"data row90 col2\" >Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model...</td>\n",
-              "      <td id=\"T_87ae0_row90_col3\" class=\"data row90 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row90_col4\" class=\"data row90 col4\" >{'n_components': {'type': '_empty', 'default': 3}}</td>\n",
-              "      <td id=\"T_87ae0_row90_col5\" class=\"data row90 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row90_col6\" class=\"data row90 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row91_col0\" class=\"data row91 col0\" >validmind.model_validation.embeddings.StabilityAnalysisKeyword</td>\n",
-              "      <td id=\"T_87ae0_row91_col1\" class=\"data row91 col1\" >Stability Analysis Keyword</td>\n",
-              "      <td id=\"T_87ae0_row91_col2\" class=\"data row91 col2\" >Evaluates robustness of embedding models to keyword swaps in the test dataset....</td>\n",
-              "      <td id=\"T_87ae0_row91_col3\" class=\"data row91 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row91_col4\" class=\"data row91 col4\" >{'keyword_dict': {'type': None, 'default': None}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_87ae0_row91_col5\" class=\"data row91 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row91_col6\" class=\"data row91 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row92_col0\" class=\"data row92 col0\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise</td>\n",
-              "      <td id=\"T_87ae0_row92_col1\" class=\"data row92 col1\" >Stability Analysis Random Noise</td>\n",
-              "      <td id=\"T_87ae0_row92_col2\" class=\"data row92 col2\" >Assesses the robustness of text embeddings models to random noise introduced via text perturbations....</td>\n",
-              "      <td id=\"T_87ae0_row92_col3\" class=\"data row92 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row92_col4\" class=\"data row92 col4\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_87ae0_row92_col5\" class=\"data row92 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row92_col6\" class=\"data row92 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row93_col0\" class=\"data row93 col0\" >validmind.model_validation.embeddings.StabilityAnalysisSynonyms</td>\n",
-              "      <td id=\"T_87ae0_row93_col1\" class=\"data row93 col1\" >Stability Analysis Synonyms</td>\n",
-              "      <td id=\"T_87ae0_row93_col2\" class=\"data row93 col2\" >Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....</td>\n",
-              "      <td id=\"T_87ae0_row93_col3\" class=\"data row93 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row93_col4\" class=\"data row93 col4\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_87ae0_row93_col5\" class=\"data row93 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row93_col6\" class=\"data row93 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row94_col0\" class=\"data row94 col0\" >validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
-              "      <td id=\"T_87ae0_row94_col1\" class=\"data row94 col1\" >Stability Analysis Translation</td>\n",
-              "      <td id=\"T_87ae0_row94_col2\" class=\"data row94 col2\" >Evaluates robustness of text embeddings models to noise introduced by translating the original text to another...</td>\n",
-              "      <td id=\"T_87ae0_row94_col3\" class=\"data row94 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row94_col4\" class=\"data row94 col4\" >{'source_lang': {'type': 'str', 'default': 'en'}, 'target_lang': {'type': 'str', 'default': 'fr'}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_87ae0_row94_col5\" class=\"data row94 col5\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row94_col6\" class=\"data row94 col6\" >['feature_extraction']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row95_col0\" class=\"data row95 col0\" >validmind.model_validation.embeddings.TSNEComponentsPairwisePlots</td>\n",
-              "      <td id=\"T_87ae0_row95_col1\" class=\"data row95 col1\" >TSNE Components Pairwise Plots</td>\n",
-              "      <td id=\"T_87ae0_row95_col2\" class=\"data row95 col2\" >Creates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential...</td>\n",
-              "      <td id=\"T_87ae0_row95_col3\" class=\"data row95 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row95_col4\" class=\"data row95 col4\" >{'n_components': {'type': '_empty', 'default': 2}, 'perplexity': {'type': '_empty', 'default': 30}, 'title': {'type': '_empty', 'default': 't-SNE'}}</td>\n",
-              "      <td id=\"T_87ae0_row95_col5\" class=\"data row95 col5\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
-              "      <td id=\"T_87ae0_row95_col6\" class=\"data row95 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row96_col0\" class=\"data row96 col0\" >validmind.model_validation.ragas.AnswerCorrectness</td>\n",
-              "      <td id=\"T_87ae0_row96_col1\" class=\"data row96 col1\" >Answer Correctness</td>\n",
-              "      <td id=\"T_87ae0_row96_col2\" class=\"data row96 col2\" >Evaluates the correctness of answers in a dataset with respect to the provided ground...</td>\n",
-              "      <td id=\"T_87ae0_row96_col3\" class=\"data row96 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row96_col4\" class=\"data row96 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}</td>\n",
-              "      <td id=\"T_87ae0_row96_col5\" class=\"data row96 col5\" >['ragas', 'llm']</td>\n",
-              "      <td id=\"T_87ae0_row96_col6\" class=\"data row96 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row97_col0\" class=\"data row97 col0\" >validmind.model_validation.ragas.AspectCritic</td>\n",
-              "      <td id=\"T_87ae0_row97_col1\" class=\"data row97 col1\" >Aspect Critic</td>\n",
-              "      <td id=\"T_87ae0_row97_col2\" class=\"data row97 col2\" >Evaluates generations against the following aspects: harmfulness, maliciousness,...</td>\n",
-              "      <td id=\"T_87ae0_row97_col3\" class=\"data row97 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row97_col4\" class=\"data row97 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'aspects': {'type': 'list', 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row97_col5\" class=\"data row97 col5\" >['ragas', 'llm', 'qualitative']</td>\n",
-              "      <td id=\"T_87ae0_row97_col6\" class=\"data row97 col6\" >['text_summarization', 'text_generation', 'text_qa']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row98_col0\" class=\"data row98 col0\" >validmind.model_validation.ragas.ContextEntityRecall</td>\n",
-              "      <td id=\"T_87ae0_row98_col1\" class=\"data row98 col1\" >Context Entity Recall</td>\n",
-              "      <td id=\"T_87ae0_row98_col2\" class=\"data row98 col2\" >Evaluates the context entity recall for dataset entries and visualizes the results....</td>\n",
-              "      <td id=\"T_87ae0_row98_col3\" class=\"data row98 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row98_col4\" class=\"data row98 col4\" >{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
-              "      <td id=\"T_87ae0_row98_col5\" class=\"data row98 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
-              "      <td id=\"T_87ae0_row98_col6\" class=\"data row98 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row99_col0\" class=\"data row99 col0\" >validmind.model_validation.ragas.ContextPrecision</td>\n",
-              "      <td id=\"T_87ae0_row99_col1\" class=\"data row99 col1\" >Context Precision</td>\n",
-              "      <td id=\"T_87ae0_row99_col2\" class=\"data row99 col2\" >Context Precision is a metric that evaluates whether all of the ground-truth...</td>\n",
-              "      <td id=\"T_87ae0_row99_col3\" class=\"data row99 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row99_col4\" class=\"data row99 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
-              "      <td id=\"T_87ae0_row99_col5\" class=\"data row99 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
-              "      <td id=\"T_87ae0_row99_col6\" class=\"data row99 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row100_col0\" class=\"data row100 col0\" >validmind.model_validation.ragas.ContextPrecisionWithoutReference</td>\n",
-              "      <td id=\"T_87ae0_row100_col1\" class=\"data row100 col1\" >Context Precision Without Reference</td>\n",
-              "      <td id=\"T_87ae0_row100_col2\" class=\"data row100 col2\" >Context Precision Without Reference is a metric used to evaluate the relevance of...</td>\n",
-              "      <td id=\"T_87ae0_row100_col3\" class=\"data row100 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row100_col4\" class=\"data row100 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}}</td>\n",
-              "      <td id=\"T_87ae0_row100_col5\" class=\"data row100 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
-              "      <td id=\"T_87ae0_row100_col6\" class=\"data row100 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row101_col0\" class=\"data row101 col0\" >validmind.model_validation.ragas.ContextRecall</td>\n",
-              "      <td id=\"T_87ae0_row101_col1\" class=\"data row101 col1\" >Context Recall</td>\n",
-              "      <td id=\"T_87ae0_row101_col2\" class=\"data row101 col2\" >Context recall measures the extent to which the retrieved context aligns with the...</td>\n",
-              "      <td id=\"T_87ae0_row101_col3\" class=\"data row101 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row101_col4\" class=\"data row101 col4\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}</td>\n",
-              "      <td id=\"T_87ae0_row101_col5\" class=\"data row101 col5\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
-              "      <td id=\"T_87ae0_row101_col6\" class=\"data row101 col6\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row102_col0\" class=\"data row102 col0\" >validmind.model_validation.ragas.Faithfulness</td>\n",
-              "      <td id=\"T_87ae0_row102_col1\" class=\"data row102 col1\" >Faithfulness</td>\n",
-              "      <td id=\"T_87ae0_row102_col2\" class=\"data row102 col2\" >Evaluates the faithfulness of the generated answers with respect to retrieved contexts....</td>\n",
-              "      <td id=\"T_87ae0_row102_col3\" class=\"data row102 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row102_col4\" class=\"data row102 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}}</td>\n",
-              "      <td id=\"T_87ae0_row102_col5\" class=\"data row102 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
-              "      <td id=\"T_87ae0_row102_col6\" class=\"data row102 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row103_col0\" class=\"data row103 col0\" >validmind.model_validation.ragas.NoiseSensitivity</td>\n",
-              "      <td id=\"T_87ae0_row103_col1\" class=\"data row103 col1\" >Noise Sensitivity</td>\n",
-              "      <td id=\"T_87ae0_row103_col2\" class=\"data row103 col2\" >Assesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it...</td>\n",
-              "      <td id=\"T_87ae0_row103_col3\" class=\"data row103 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row103_col4\" class=\"data row103 col4\" >{'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}, 'reference_column': {'type': '_empty', 'default': 'reference'}, 'focus': {'type': '_empty', 'default': 'relevant'}, 'user_input_column': {'type': '_empty', 'default': 'user_input'}}</td>\n",
-              "      <td id=\"T_87ae0_row103_col5\" class=\"data row103 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
-              "      <td id=\"T_87ae0_row103_col6\" class=\"data row103 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row104_col0\" class=\"data row104 col0\" >validmind.model_validation.ragas.ResponseRelevancy</td>\n",
-              "      <td id=\"T_87ae0_row104_col1\" class=\"data row104 col1\" >Response Relevancy</td>\n",
-              "      <td id=\"T_87ae0_row104_col2\" class=\"data row104 col2\" >Assesses how pertinent the generated answer is to the given prompt....</td>\n",
-              "      <td id=\"T_87ae0_row104_col3\" class=\"data row104 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row104_col4\" class=\"data row104 col4\" >{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'response_column': {'type': '_empty', 'default': 'response'}}</td>\n",
-              "      <td id=\"T_87ae0_row104_col5\" class=\"data row104 col5\" >['ragas', 'llm', 'rag_performance']</td>\n",
-              "      <td id=\"T_87ae0_row104_col6\" class=\"data row104 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row105_col0\" class=\"data row105 col0\" >validmind.model_validation.ragas.SemanticSimilarity</td>\n",
-              "      <td id=\"T_87ae0_row105_col1\" class=\"data row105 col1\" >Semantic Similarity</td>\n",
-              "      <td id=\"T_87ae0_row105_col2\" class=\"data row105 col2\" >Calculates the semantic similarity between generated responses and ground truths...</td>\n",
-              "      <td id=\"T_87ae0_row105_col3\" class=\"data row105 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row105_col4\" class=\"data row105 col4\" >{'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}</td>\n",
-              "      <td id=\"T_87ae0_row105_col5\" class=\"data row105 col5\" >['ragas', 'llm']</td>\n",
-              "      <td id=\"T_87ae0_row105_col6\" class=\"data row105 col6\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row106_col0\" class=\"data row106 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
-              "      <td id=\"T_87ae0_row106_col1\" class=\"data row106 col1\" >Adjusted Mutual Information</td>\n",
-              "      <td id=\"T_87ae0_row106_col2\" class=\"data row106 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
-              "      <td id=\"T_87ae0_row106_col3\" class=\"data row106 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row106_col4\" class=\"data row106 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row106_col5\" class=\"data row106 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_87ae0_row106_col6\" class=\"data row106 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row107_col0\" class=\"data row107 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
-              "      <td id=\"T_87ae0_row107_col1\" class=\"data row107 col1\" >Adjusted Rand Index</td>\n",
-              "      <td id=\"T_87ae0_row107_col2\" class=\"data row107 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
-              "      <td id=\"T_87ae0_row107_col3\" class=\"data row107 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row107_col4\" class=\"data row107 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row107_col5\" class=\"data row107 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_87ae0_row107_col6\" class=\"data row107 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row108_col0\" class=\"data row108 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
-              "      <td id=\"T_87ae0_row108_col1\" class=\"data row108 col1\" >Calibration Curve</td>\n",
-              "      <td id=\"T_87ae0_row108_col2\" class=\"data row108 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
-              "      <td id=\"T_87ae0_row108_col3\" class=\"data row108 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row108_col4\" class=\"data row108 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_87ae0_row108_col5\" class=\"data row108 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
-              "      <td id=\"T_87ae0_row108_col6\" class=\"data row108 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row109_col0\" class=\"data row109 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_87ae0_row109_col1\" class=\"data row109 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_87ae0_row109_col2\" class=\"data row109 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_87ae0_row109_col3\" class=\"data row109 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row109_col4\" class=\"data row109 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
-              "      <td id=\"T_87ae0_row109_col5\" class=\"data row109 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row109_col6\" class=\"data row109 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row110_col0\" class=\"data row110 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
-              "      <td id=\"T_87ae0_row110_col1\" class=\"data row110 col1\" >Classifier Threshold Optimization</td>\n",
-              "      <td id=\"T_87ae0_row110_col2\" class=\"data row110 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
-              "      <td id=\"T_87ae0_row110_col3\" class=\"data row110 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row110_col4\" class=\"data row110 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row110_col5\" class=\"data row110 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
-              "      <td id=\"T_87ae0_row110_col6\" class=\"data row110 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row111_col0\" class=\"data row111 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
-              "      <td id=\"T_87ae0_row111_col1\" class=\"data row111 col1\" >Cluster Cosine Similarity</td>\n",
-              "      <td id=\"T_87ae0_row111_col2\" class=\"data row111 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
-              "      <td id=\"T_87ae0_row111_col3\" class=\"data row111 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row111_col4\" class=\"data row111 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row111_col5\" class=\"data row111 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_87ae0_row111_col6\" class=\"data row111 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row112_col0\" class=\"data row112 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
-              "      <td id=\"T_87ae0_row112_col1\" class=\"data row112 col1\" >Cluster Performance Metrics</td>\n",
-              "      <td id=\"T_87ae0_row112_col2\" class=\"data row112 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
-              "      <td id=\"T_87ae0_row112_col3\" class=\"data row112 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row112_col4\" class=\"data row112 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row112_col5\" class=\"data row112 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_87ae0_row112_col6\" class=\"data row112 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row113_col0\" class=\"data row113 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
-              "      <td id=\"T_87ae0_row113_col1\" class=\"data row113 col1\" >Completeness Score</td>\n",
-              "      <td id=\"T_87ae0_row113_col2\" class=\"data row113 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
-              "      <td id=\"T_87ae0_row113_col3\" class=\"data row113 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row113_col4\" class=\"data row113 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row113_col5\" class=\"data row113 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_87ae0_row113_col6\" class=\"data row113 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row114_col0\" class=\"data row114 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_87ae0_row114_col1\" class=\"data row114 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_87ae0_row114_col2\" class=\"data row114 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_87ae0_row114_col3\" class=\"data row114 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row114_col4\" class=\"data row114 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_87ae0_row114_col5\" class=\"data row114 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row114_col6\" class=\"data row114 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row115_col0\" class=\"data row115 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
-              "      <td id=\"T_87ae0_row115_col1\" class=\"data row115 col1\" >Feature Importance</td>\n",
-              "      <td id=\"T_87ae0_row115_col2\" class=\"data row115 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
-              "      <td id=\"T_87ae0_row115_col3\" class=\"data row115 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row115_col4\" class=\"data row115 col4\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
-              "      <td id=\"T_87ae0_row115_col5\" class=\"data row115 col5\" >['model_explainability', 'sklearn']</td>\n",
-              "      <td id=\"T_87ae0_row115_col6\" class=\"data row115 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row116_col0\" class=\"data row116 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
-              "      <td id=\"T_87ae0_row116_col1\" class=\"data row116 col1\" >Fowlkes Mallows Score</td>\n",
-              "      <td id=\"T_87ae0_row116_col2\" class=\"data row116 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
-              "      <td id=\"T_87ae0_row116_col3\" class=\"data row116 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row116_col4\" class=\"data row116 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row116_col5\" class=\"data row116 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row116_col6\" class=\"data row116 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row117_col0\" class=\"data row117 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
-              "      <td id=\"T_87ae0_row117_col1\" class=\"data row117 col1\" >Homogeneity Score</td>\n",
-              "      <td id=\"T_87ae0_row117_col2\" class=\"data row117 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
-              "      <td id=\"T_87ae0_row117_col3\" class=\"data row117 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row117_col4\" class=\"data row117 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row117_col5\" class=\"data row117 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row117_col6\" class=\"data row117 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row118_col0\" class=\"data row118 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_87ae0_row118_col1\" class=\"data row118 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_87ae0_row118_col2\" class=\"data row118 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
-              "      <td id=\"T_87ae0_row118_col3\" class=\"data row118 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row118_col4\" class=\"data row118 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row118_col5\" class=\"data row118 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row118_col6\" class=\"data row118 col6\" >['clustering', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row119_col0\" class=\"data row119 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
-              "      <td id=\"T_87ae0_row119_col1\" class=\"data row119 col1\" >K Means Clusters Optimization</td>\n",
-              "      <td id=\"T_87ae0_row119_col2\" class=\"data row119 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
-              "      <td id=\"T_87ae0_row119_col3\" class=\"data row119 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row119_col4\" class=\"data row119 col4\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row119_col5\" class=\"data row119 col5\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
-              "      <td id=\"T_87ae0_row119_col6\" class=\"data row119 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row120_col0\" class=\"data row120 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_87ae0_row120_col1\" class=\"data row120 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_87ae0_row120_col2\" class=\"data row120 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_87ae0_row120_col3\" class=\"data row120 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row120_col4\" class=\"data row120 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_87ae0_row120_col5\" class=\"data row120 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row120_col6\" class=\"data row120 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row121_col0\" class=\"data row121 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_87ae0_row121_col1\" class=\"data row121 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_87ae0_row121_col2\" class=\"data row121 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
-              "      <td id=\"T_87ae0_row121_col3\" class=\"data row121 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row121_col4\" class=\"data row121 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_87ae0_row121_col5\" class=\"data row121 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row121_col6\" class=\"data row121 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row122_col0\" class=\"data row122 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_87ae0_row122_col1\" class=\"data row122 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_87ae0_row122_col2\" class=\"data row122 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_87ae0_row122_col3\" class=\"data row122 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row122_col4\" class=\"data row122 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_87ae0_row122_col5\" class=\"data row122 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row122_col6\" class=\"data row122 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row123_col0\" class=\"data row123 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
-              "      <td id=\"T_87ae0_row123_col1\" class=\"data row123 col1\" >Model Parameters</td>\n",
-              "      <td id=\"T_87ae0_row123_col2\" class=\"data row123 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
-              "      <td id=\"T_87ae0_row123_col3\" class=\"data row123 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row123_col4\" class=\"data row123 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row123_col5\" class=\"data row123 col5\" >['model_training', 'metadata']</td>\n",
-              "      <td id=\"T_87ae0_row123_col6\" class=\"data row123 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row124_col0\" class=\"data row124 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_87ae0_row124_col1\" class=\"data row124 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_87ae0_row124_col2\" class=\"data row124 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_87ae0_row124_col3\" class=\"data row124 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_87ae0_row124_col4\" class=\"data row124 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row124_col5\" class=\"data row124 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
-              "      <td id=\"T_87ae0_row124_col6\" class=\"data row124 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row125_col0\" class=\"data row125 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_87ae0_row125_col1\" class=\"data row125 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_87ae0_row125_col2\" class=\"data row125 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
-              "      <td id=\"T_87ae0_row125_col3\" class=\"data row125 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_87ae0_row125_col4\" class=\"data row125 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
-              "      <td id=\"T_87ae0_row125_col5\" class=\"data row125 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
-              "      <td id=\"T_87ae0_row125_col6\" class=\"data row125 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row126_col0\" class=\"data row126 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_87ae0_row126_col1\" class=\"data row126 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_87ae0_row126_col2\" class=\"data row126 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_87ae0_row126_col3\" class=\"data row126 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row126_col4\" class=\"data row126 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row126_col5\" class=\"data row126 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row126_col6\" class=\"data row126 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row127_col0\" class=\"data row127 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_87ae0_row127_col1\" class=\"data row127 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_87ae0_row127_col2\" class=\"data row127 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_87ae0_row127_col3\" class=\"data row127 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row127_col4\" class=\"data row127 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
-              "      <td id=\"T_87ae0_row127_col5\" class=\"data row127 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row127_col6\" class=\"data row127 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row128_col0\" class=\"data row128 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_87ae0_row128_col1\" class=\"data row128 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_87ae0_row128_col2\" class=\"data row128 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_87ae0_row128_col3\" class=\"data row128 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row128_col4\" class=\"data row128 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row128_col5\" class=\"data row128 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row128_col6\" class=\"data row128 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row129_col0\" class=\"data row129 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_87ae0_row129_col1\" class=\"data row129 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_87ae0_row129_col2\" class=\"data row129 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_87ae0_row129_col3\" class=\"data row129 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row129_col4\" class=\"data row129 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row129_col5\" class=\"data row129 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row129_col6\" class=\"data row129 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row130_col0\" class=\"data row130 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
-              "      <td id=\"T_87ae0_row130_col1\" class=\"data row130 col1\" >Regression Errors</td>\n",
-              "      <td id=\"T_87ae0_row130_col2\" class=\"data row130 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
-              "      <td id=\"T_87ae0_row130_col3\" class=\"data row130 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row130_col4\" class=\"data row130 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row130_col5\" class=\"data row130 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row130_col6\" class=\"data row130 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row131_col0\" class=\"data row131 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
-              "      <td id=\"T_87ae0_row131_col1\" class=\"data row131 col1\" >Regression Errors Comparison</td>\n",
-              "      <td id=\"T_87ae0_row131_col2\" class=\"data row131 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
-              "      <td id=\"T_87ae0_row131_col3\" class=\"data row131 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_87ae0_row131_col4\" class=\"data row131 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row131_col5\" class=\"data row131 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_87ae0_row131_col6\" class=\"data row131 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row132_col0\" class=\"data row132 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
-              "      <td id=\"T_87ae0_row132_col1\" class=\"data row132 col1\" >Regression Performance</td>\n",
-              "      <td id=\"T_87ae0_row132_col2\" class=\"data row132 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
-              "      <td id=\"T_87ae0_row132_col3\" class=\"data row132 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row132_col4\" class=\"data row132 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row132_col5\" class=\"data row132 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row132_col6\" class=\"data row132 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row133_col0\" class=\"data row133 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
-              "      <td id=\"T_87ae0_row133_col1\" class=\"data row133 col1\" >Regression R2 Square</td>\n",
-              "      <td id=\"T_87ae0_row133_col2\" class=\"data row133 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
-              "      <td id=\"T_87ae0_row133_col3\" class=\"data row133 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row133_col4\" class=\"data row133 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row133_col5\" class=\"data row133 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row133_col6\" class=\"data row133 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row134_col0\" class=\"data row134 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
-              "      <td id=\"T_87ae0_row134_col1\" class=\"data row134 col1\" >Regression R2 Square Comparison</td>\n",
-              "      <td id=\"T_87ae0_row134_col2\" class=\"data row134 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
-              "      <td id=\"T_87ae0_row134_col3\" class=\"data row134 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_87ae0_row134_col4\" class=\"data row134 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row134_col5\" class=\"data row134 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_87ae0_row134_col6\" class=\"data row134 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row135_col0\" class=\"data row135 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_87ae0_row135_col1\" class=\"data row135 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_87ae0_row135_col2\" class=\"data row135 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
-              "      <td id=\"T_87ae0_row135_col3\" class=\"data row135 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row135_col4\" class=\"data row135 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_87ae0_row135_col5\" class=\"data row135 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row135_col6\" class=\"data row135 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row136_col0\" class=\"data row136 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_87ae0_row136_col1\" class=\"data row136 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_87ae0_row136_col2\" class=\"data row136 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_87ae0_row136_col3\" class=\"data row136 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row136_col4\" class=\"data row136 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row136_col5\" class=\"data row136 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row136_col6\" class=\"data row136 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row137_col0\" class=\"data row137 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
-              "      <td id=\"T_87ae0_row137_col1\" class=\"data row137 col1\" >Score Probability Alignment</td>\n",
-              "      <td id=\"T_87ae0_row137_col2\" class=\"data row137 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
-              "      <td id=\"T_87ae0_row137_col3\" class=\"data row137 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row137_col4\" class=\"data row137 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_87ae0_row137_col5\" class=\"data row137 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
-              "      <td id=\"T_87ae0_row137_col6\" class=\"data row137 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row138_col0\" class=\"data row138 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
-              "      <td id=\"T_87ae0_row138_col1\" class=\"data row138 col1\" >Silhouette Plot</td>\n",
-              "      <td id=\"T_87ae0_row138_col2\" class=\"data row138 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
-              "      <td id=\"T_87ae0_row138_col3\" class=\"data row138 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row138_col4\" class=\"data row138 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row138_col5\" class=\"data row138 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row138_col6\" class=\"data row138 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row139_col0\" class=\"data row139 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_87ae0_row139_col1\" class=\"data row139 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_87ae0_row139_col2\" class=\"data row139 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_87ae0_row139_col3\" class=\"data row139 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row139_col4\" class=\"data row139 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_87ae0_row139_col5\" class=\"data row139 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row139_col6\" class=\"data row139 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row140_col0\" class=\"data row140 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
-              "      <td id=\"T_87ae0_row140_col1\" class=\"data row140 col1\" >V Measure</td>\n",
-              "      <td id=\"T_87ae0_row140_col2\" class=\"data row140 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
-              "      <td id=\"T_87ae0_row140_col3\" class=\"data row140 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row140_col4\" class=\"data row140 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row140_col5\" class=\"data row140 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row140_col6\" class=\"data row140 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row141_col0\" class=\"data row141 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_87ae0_row141_col1\" class=\"data row141 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_87ae0_row141_col2\" class=\"data row141 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_87ae0_row141_col3\" class=\"data row141 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row141_col4\" class=\"data row141 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row141_col5\" class=\"data row141 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row141_col6\" class=\"data row141 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row142_col0\" class=\"data row142 col0\" >validmind.model_validation.statsmodels.AutoARIMA</td>\n",
-              "      <td id=\"T_87ae0_row142_col1\" class=\"data row142 col1\" >Auto ARIMA</td>\n",
-              "      <td id=\"T_87ae0_row142_col2\" class=\"data row142 col2\" >Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....</td>\n",
-              "      <td id=\"T_87ae0_row142_col3\" class=\"data row142 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row142_col4\" class=\"data row142 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row142_col5\" class=\"data row142 col5\" >['time_series_data', 'forecasting', 'model_selection', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row142_col6\" class=\"data row142 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row143_col0\" class=\"data row143 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
-              "      <td id=\"T_87ae0_row143_col1\" class=\"data row143 col1\" >Cumulative Prediction Probabilities</td>\n",
-              "      <td id=\"T_87ae0_row143_col2\" class=\"data row143 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
-              "      <td id=\"T_87ae0_row143_col3\" class=\"data row143 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row143_col4\" class=\"data row143 col4\" >{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}</td>\n",
-              "      <td id=\"T_87ae0_row143_col5\" class=\"data row143 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_87ae0_row143_col6\" class=\"data row143 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row144_col0\" class=\"data row144 col0\" >validmind.model_validation.statsmodels.DurbinWatsonTest</td>\n",
-              "      <td id=\"T_87ae0_row144_col1\" class=\"data row144 col1\" >Durbin Watson Test</td>\n",
-              "      <td id=\"T_87ae0_row144_col2\" class=\"data row144 col2\" >Assesses autocorrelation in time series data features using the Durbin-Watson statistic....</td>\n",
-              "      <td id=\"T_87ae0_row144_col3\" class=\"data row144 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row144_col4\" class=\"data row144 col4\" >{'threshold': {'type': '_empty', 'default': [1.5, 2.5]}}</td>\n",
-              "      <td id=\"T_87ae0_row144_col5\" class=\"data row144 col5\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row144_col6\" class=\"data row144 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row145_col0\" class=\"data row145 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
-              "      <td id=\"T_87ae0_row145_col1\" class=\"data row145 col1\" >GINI Table</td>\n",
-              "      <td id=\"T_87ae0_row145_col2\" class=\"data row145 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
-              "      <td id=\"T_87ae0_row145_col3\" class=\"data row145 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row145_col4\" class=\"data row145 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row145_col5\" class=\"data row145 col5\" >['model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row145_col6\" class=\"data row145 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row146_col0\" class=\"data row146 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
-              "      <td id=\"T_87ae0_row146_col1\" class=\"data row146 col1\" >Kolmogorov Smirnov</td>\n",
-              "      <td id=\"T_87ae0_row146_col2\" class=\"data row146 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
-              "      <td id=\"T_87ae0_row146_col3\" class=\"data row146 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row146_col4\" class=\"data row146 col4\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
-              "      <td id=\"T_87ae0_row146_col5\" class=\"data row146 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row146_col6\" class=\"data row146 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row147_col0\" class=\"data row147 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
-              "      <td id=\"T_87ae0_row147_col1\" class=\"data row147 col1\" >Lilliefors</td>\n",
-              "      <td id=\"T_87ae0_row147_col2\" class=\"data row147 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
-              "      <td id=\"T_87ae0_row147_col3\" class=\"data row147 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row147_col4\" class=\"data row147 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row147_col5\" class=\"data row147 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_87ae0_row147_col6\" class=\"data row147 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row148_col0\" class=\"data row148 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
-              "      <td id=\"T_87ae0_row148_col1\" class=\"data row148 col1\" >Prediction Probabilities Histogram</td>\n",
-              "      <td id=\"T_87ae0_row148_col2\" class=\"data row148 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
-              "      <td id=\"T_87ae0_row148_col3\" class=\"data row148 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row148_col4\" class=\"data row148 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
-              "      <td id=\"T_87ae0_row148_col5\" class=\"data row148 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_87ae0_row148_col6\" class=\"data row148 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row149_col0\" class=\"data row149 col0\" >validmind.model_validation.statsmodels.RegressionCoeffs</td>\n",
-              "      <td id=\"T_87ae0_row149_col1\" class=\"data row149 col1\" >Regression Coeffs</td>\n",
-              "      <td id=\"T_87ae0_row149_col2\" class=\"data row149 col2\" >Assesses the significance and uncertainty of predictor variables in a regression model through visualization of...</td>\n",
-              "      <td id=\"T_87ae0_row149_col3\" class=\"data row149 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row149_col4\" class=\"data row149 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row149_col5\" class=\"data row149 col5\" >['tabular_data', 'visualization', 'model_training']</td>\n",
-              "      <td id=\"T_87ae0_row149_col6\" class=\"data row149 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row150_col0\" class=\"data row150 col0\" >validmind.model_validation.statsmodels.RegressionFeatureSignificance</td>\n",
-              "      <td id=\"T_87ae0_row150_col1\" class=\"data row150 col1\" >Regression Feature Significance</td>\n",
-              "      <td id=\"T_87ae0_row150_col2\" class=\"data row150 col2\" >Assesses and visualizes the statistical significance of features in a regression model....</td>\n",
-              "      <td id=\"T_87ae0_row150_col3\" class=\"data row150 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row150_col4\" class=\"data row150 col4\" >{'fontsize': {'type': 'int', 'default': 10}, 'p_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_87ae0_row150_col5\" class=\"data row150 col5\" >['statistical_test', 'model_interpretation', 'visualization', 'feature_importance']</td>\n",
-              "      <td id=\"T_87ae0_row150_col6\" class=\"data row150 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row151_col0\" class=\"data row151 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlot</td>\n",
-              "      <td id=\"T_87ae0_row151_col1\" class=\"data row151 col1\" >Regression Model Forecast Plot</td>\n",
-              "      <td id=\"T_87ae0_row151_col2\" class=\"data row151 col2\" >Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over...</td>\n",
-              "      <td id=\"T_87ae0_row151_col3\" class=\"data row151 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row151_col4\" class=\"data row151 col4\" >{'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row151_col5\" class=\"data row151 col5\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row151_col6\" class=\"data row151 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row152_col0\" class=\"data row152 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels</td>\n",
-              "      <td id=\"T_87ae0_row152_col1\" class=\"data row152 col1\" >Regression Model Forecast Plot Levels</td>\n",
-              "      <td id=\"T_87ae0_row152_col2\" class=\"data row152 col2\" >Assesses the alignment between forecasted and observed values in regression models through visual plots...</td>\n",
-              "      <td id=\"T_87ae0_row152_col3\" class=\"data row152 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row152_col4\" class=\"data row152 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row152_col5\" class=\"data row152 col5\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row152_col6\" class=\"data row152 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row153_col0\" class=\"data row153 col0\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
-              "      <td id=\"T_87ae0_row153_col1\" class=\"data row153 col1\" >Regression Model Sensitivity Plot</td>\n",
-              "      <td id=\"T_87ae0_row153_col2\" class=\"data row153 col2\" >Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and...</td>\n",
-              "      <td id=\"T_87ae0_row153_col3\" class=\"data row153 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row153_col4\" class=\"data row153 col4\" >{'shocks': {'type': None, 'default': [0.1]}, 'transformation': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_87ae0_row153_col5\" class=\"data row153 col5\" >['senstivity_analysis', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row153_col6\" class=\"data row153 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row154_col0\" class=\"data row154 col0\" >validmind.model_validation.statsmodels.RegressionModelSummary</td>\n",
-              "      <td id=\"T_87ae0_row154_col1\" class=\"data row154 col1\" >Regression Model Summary</td>\n",
-              "      <td id=\"T_87ae0_row154_col2\" class=\"data row154 col2\" >Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....</td>\n",
-              "      <td id=\"T_87ae0_row154_col3\" class=\"data row154 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row154_col4\" class=\"data row154 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row154_col5\" class=\"data row154 col5\" >['model_performance', 'regression']</td>\n",
-              "      <td id=\"T_87ae0_row154_col6\" class=\"data row154 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row155_col0\" class=\"data row155 col0\" >validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance</td>\n",
-              "      <td id=\"T_87ae0_row155_col1\" class=\"data row155 col1\" >Regression Permutation Feature Importance</td>\n",
-              "      <td id=\"T_87ae0_row155_col2\" class=\"data row155 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_87ae0_row155_col3\" class=\"data row155 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row155_col4\" class=\"data row155 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
-              "      <td id=\"T_87ae0_row155_col5\" class=\"data row155 col5\" >['statsmodels', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row155_col6\" class=\"data row155 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row156_col0\" class=\"data row156 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
-              "      <td id=\"T_87ae0_row156_col1\" class=\"data row156 col1\" >Scorecard Histogram</td>\n",
-              "      <td id=\"T_87ae0_row156_col2\" class=\"data row156 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
-              "      <td id=\"T_87ae0_row156_col3\" class=\"data row156 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_87ae0_row156_col4\" class=\"data row156 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}</td>\n",
-              "      <td id=\"T_87ae0_row156_col5\" class=\"data row156 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
-              "      <td id=\"T_87ae0_row156_col6\" class=\"data row156 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row157_col0\" class=\"data row157 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
-              "      <td id=\"T_87ae0_row157_col1\" class=\"data row157 col1\" >Calibration Curve Drift</td>\n",
-              "      <td id=\"T_87ae0_row157_col2\" class=\"data row157 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row157_col3\" class=\"data row157 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row157_col4\" class=\"data row157 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row157_col5\" class=\"data row157 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row157_col6\" class=\"data row157 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row158_col0\" class=\"data row158 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
-              "      <td id=\"T_87ae0_row158_col1\" class=\"data row158 col1\" >Class Discrimination Drift</td>\n",
-              "      <td id=\"T_87ae0_row158_col2\" class=\"data row158 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row158_col3\" class=\"data row158 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row158_col4\" class=\"data row158 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row158_col5\" class=\"data row158 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row158_col6\" class=\"data row158 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row159_col0\" class=\"data row159 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
-              "      <td id=\"T_87ae0_row159_col1\" class=\"data row159 col1\" >Class Imbalance Drift</td>\n",
-              "      <td id=\"T_87ae0_row159_col2\" class=\"data row159 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row159_col3\" class=\"data row159 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_87ae0_row159_col4\" class=\"data row159 col4\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
-              "      <td id=\"T_87ae0_row159_col5\" class=\"data row159 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
-              "      <td id=\"T_87ae0_row159_col6\" class=\"data row159 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row160_col0\" class=\"data row160 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
-              "      <td id=\"T_87ae0_row160_col1\" class=\"data row160 col1\" >Classification Accuracy Drift</td>\n",
-              "      <td id=\"T_87ae0_row160_col2\" class=\"data row160 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row160_col3\" class=\"data row160 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row160_col4\" class=\"data row160 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row160_col5\" class=\"data row160 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row160_col6\" class=\"data row160 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row161_col0\" class=\"data row161 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
-              "      <td id=\"T_87ae0_row161_col1\" class=\"data row161 col1\" >Confusion Matrix Drift</td>\n",
-              "      <td id=\"T_87ae0_row161_col2\" class=\"data row161 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row161_col3\" class=\"data row161 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row161_col4\" class=\"data row161 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row161_col5\" class=\"data row161 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_87ae0_row161_col6\" class=\"data row161 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row162_col0\" class=\"data row162 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
-              "      <td id=\"T_87ae0_row162_col1\" class=\"data row162 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
-              "      <td id=\"T_87ae0_row162_col2\" class=\"data row162 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row162_col3\" class=\"data row162 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row162_col4\" class=\"data row162 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row162_col5\" class=\"data row162 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_87ae0_row162_col6\" class=\"data row162 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row163_col0\" class=\"data row163 col0\" >validmind.ongoing_monitoring.FeatureDrift</td>\n",
-              "      <td id=\"T_87ae0_row163_col1\" class=\"data row163 col1\" >Feature Drift</td>\n",
-              "      <td id=\"T_87ae0_row163_col2\" class=\"data row163 col2\" >Evaluates changes in feature distribution over time to identify potential model drift....</td>\n",
-              "      <td id=\"T_87ae0_row163_col3\" class=\"data row163 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_87ae0_row163_col4\" class=\"data row163 col4\" >{'bins': {'type': '_empty', 'default': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, 'feature_columns': {'type': '_empty', 'default': None}, 'psi_threshold': {'type': '_empty', 'default': 0.2}}</td>\n",
-              "      <td id=\"T_87ae0_row163_col5\" class=\"data row163 col5\" >['visualization']</td>\n",
-              "      <td id=\"T_87ae0_row163_col6\" class=\"data row163 col6\" >['monitoring']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row164_col0\" class=\"data row164 col0\" >validmind.ongoing_monitoring.PredictionAcrossEachFeature</td>\n",
-              "      <td id=\"T_87ae0_row164_col1\" class=\"data row164 col1\" >Prediction Across Each Feature</td>\n",
-              "      <td id=\"T_87ae0_row164_col2\" class=\"data row164 col2\" >Assesses differences in model predictions across individual features between reference and monitoring datasets...</td>\n",
-              "      <td id=\"T_87ae0_row164_col3\" class=\"data row164 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row164_col4\" class=\"data row164 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row164_col5\" class=\"data row164 col5\" >['visualization']</td>\n",
-              "      <td id=\"T_87ae0_row164_col6\" class=\"data row164 col6\" >['monitoring']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row165_col0\" class=\"data row165 col0\" >validmind.ongoing_monitoring.PredictionCorrelation</td>\n",
-              "      <td id=\"T_87ae0_row165_col1\" class=\"data row165 col1\" >Prediction Correlation</td>\n",
-              "      <td id=\"T_87ae0_row165_col2\" class=\"data row165 col2\" >Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential...</td>\n",
-              "      <td id=\"T_87ae0_row165_col3\" class=\"data row165 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row165_col4\" class=\"data row165 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row165_col5\" class=\"data row165 col5\" >['visualization']</td>\n",
-              "      <td id=\"T_87ae0_row165_col6\" class=\"data row165 col6\" >['monitoring']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row166_col0\" class=\"data row166 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
-              "      <td id=\"T_87ae0_row166_col1\" class=\"data row166 col1\" >Prediction Probabilities Histogram Drift</td>\n",
-              "      <td id=\"T_87ae0_row166_col2\" class=\"data row166 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row166_col3\" class=\"data row166 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row166_col4\" class=\"data row166 col4\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_87ae0_row166_col5\" class=\"data row166 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_87ae0_row166_col6\" class=\"data row166 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row167_col0\" class=\"data row167 col0\" >validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures</td>\n",
-              "      <td id=\"T_87ae0_row167_col1\" class=\"data row167 col1\" >Prediction Quantiles Across Features</td>\n",
-              "      <td id=\"T_87ae0_row167_col2\" class=\"data row167 col2\" >Assesses differences in model prediction distributions across individual features between reference...</td>\n",
-              "      <td id=\"T_87ae0_row167_col3\" class=\"data row167 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row167_col4\" class=\"data row167 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row167_col5\" class=\"data row167 col5\" >['visualization']</td>\n",
-              "      <td id=\"T_87ae0_row167_col6\" class=\"data row167 col6\" >['monitoring']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row168_col0\" class=\"data row168 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
-              "      <td id=\"T_87ae0_row168_col1\" class=\"data row168 col1\" >ROC Curve Drift</td>\n",
-              "      <td id=\"T_87ae0_row168_col2\" class=\"data row168 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_87ae0_row168_col3\" class=\"data row168 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row168_col4\" class=\"data row168 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row168_col5\" class=\"data row168 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_87ae0_row168_col6\" class=\"data row168 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row169_col0\" class=\"data row169 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
-              "      <td id=\"T_87ae0_row169_col1\" class=\"data row169 col1\" >Score Bands Drift</td>\n",
-              "      <td id=\"T_87ae0_row169_col2\" class=\"data row169 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
-              "      <td id=\"T_87ae0_row169_col3\" class=\"data row169 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row169_col4\" class=\"data row169 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_87ae0_row169_col5\" class=\"data row169 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
-              "      <td id=\"T_87ae0_row169_col6\" class=\"data row169 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row170_col0\" class=\"data row170 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
-              "      <td id=\"T_87ae0_row170_col1\" class=\"data row170 col1\" >Scorecard Histogram Drift</td>\n",
-              "      <td id=\"T_87ae0_row170_col2\" class=\"data row170 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
-              "      <td id=\"T_87ae0_row170_col3\" class=\"data row170 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_87ae0_row170_col4\" class=\"data row170 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_87ae0_row170_col5\" class=\"data row170 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
-              "      <td id=\"T_87ae0_row170_col6\" class=\"data row170 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row171_col0\" class=\"data row171 col0\" >validmind.ongoing_monitoring.TargetPredictionDistributionPlot</td>\n",
-              "      <td id=\"T_87ae0_row171_col1\" class=\"data row171 col1\" >Target Prediction Distribution Plot</td>\n",
-              "      <td id=\"T_87ae0_row171_col2\" class=\"data row171 col2\" >Assesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify...</td>\n",
-              "      <td id=\"T_87ae0_row171_col3\" class=\"data row171 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row171_col4\" class=\"data row171 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_87ae0_row171_col5\" class=\"data row171 col5\" >['visualization']</td>\n",
-              "      <td id=\"T_87ae0_row171_col6\" class=\"data row171 col6\" >['monitoring']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row172_col0\" class=\"data row172 col0\" >validmind.prompt_validation.Bias</td>\n",
-              "      <td id=\"T_87ae0_row172_col1\" class=\"data row172 col1\" >Bias</td>\n",
-              "      <td id=\"T_87ae0_row172_col2\" class=\"data row172 col2\" >Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...</td>\n",
-              "      <td id=\"T_87ae0_row172_col3\" class=\"data row172 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row172_col4\" class=\"data row172 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row172_col5\" class=\"data row172 col5\" >['llm', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row172_col6\" class=\"data row172 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row173_col0\" class=\"data row173 col0\" >validmind.prompt_validation.Clarity</td>\n",
-              "      <td id=\"T_87ae0_row173_col1\" class=\"data row173 col1\" >Clarity</td>\n",
-              "      <td id=\"T_87ae0_row173_col2\" class=\"data row173 col2\" >Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....</td>\n",
-              "      <td id=\"T_87ae0_row173_col3\" class=\"data row173 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row173_col4\" class=\"data row173 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row173_col5\" class=\"data row173 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row173_col6\" class=\"data row173 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row174_col0\" class=\"data row174 col0\" >validmind.prompt_validation.Conciseness</td>\n",
-              "      <td id=\"T_87ae0_row174_col1\" class=\"data row174 col1\" >Conciseness</td>\n",
-              "      <td id=\"T_87ae0_row174_col2\" class=\"data row174 col2\" >Analyzes and grades the conciseness of prompts provided to a Large Language Model....</td>\n",
-              "      <td id=\"T_87ae0_row174_col3\" class=\"data row174 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row174_col4\" class=\"data row174 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row174_col5\" class=\"data row174 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row174_col6\" class=\"data row174 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row175_col0\" class=\"data row175 col0\" >validmind.prompt_validation.Delimitation</td>\n",
-              "      <td id=\"T_87ae0_row175_col1\" class=\"data row175 col1\" >Delimitation</td>\n",
-              "      <td id=\"T_87ae0_row175_col2\" class=\"data row175 col2\" >Evaluates the proper use of delimiters in prompts provided to Large Language Models....</td>\n",
-              "      <td id=\"T_87ae0_row175_col3\" class=\"data row175 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row175_col4\" class=\"data row175 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row175_col5\" class=\"data row175 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row175_col6\" class=\"data row175 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row176_col0\" class=\"data row176 col0\" >validmind.prompt_validation.NegativeInstruction</td>\n",
-              "      <td id=\"T_87ae0_row176_col1\" class=\"data row176 col1\" >Negative Instruction</td>\n",
-              "      <td id=\"T_87ae0_row176_col2\" class=\"data row176 col2\" >Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....</td>\n",
-              "      <td id=\"T_87ae0_row176_col3\" class=\"data row176 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row176_col4\" class=\"data row176 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row176_col5\" class=\"data row176 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row176_col6\" class=\"data row176 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row177_col0\" class=\"data row177 col0\" >validmind.prompt_validation.Robustness</td>\n",
-              "      <td id=\"T_87ae0_row177_col1\" class=\"data row177 col1\" >Robustness</td>\n",
-              "      <td id=\"T_87ae0_row177_col2\" class=\"data row177 col2\" >Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test...</td>\n",
-              "      <td id=\"T_87ae0_row177_col3\" class=\"data row177 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row177_col4\" class=\"data row177 col4\" >{'num_tests': {'type': '_empty', 'default': 10}}</td>\n",
-              "      <td id=\"T_87ae0_row177_col5\" class=\"data row177 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row177_col6\" class=\"data row177 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row178_col0\" class=\"data row178 col0\" >validmind.prompt_validation.Specificity</td>\n",
-              "      <td id=\"T_87ae0_row178_col1\" class=\"data row178 col1\" >Specificity</td>\n",
-              "      <td id=\"T_87ae0_row178_col2\" class=\"data row178 col2\" >Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,...</td>\n",
-              "      <td id=\"T_87ae0_row178_col3\" class=\"data row178 col3\" >['model']</td>\n",
-              "      <td id=\"T_87ae0_row178_col4\" class=\"data row178 col4\" >{'min_threshold': {'type': '_empty', 'default': 7}}</td>\n",
-              "      <td id=\"T_87ae0_row178_col5\" class=\"data row178 col5\" >['llm', 'zero_shot', 'few_shot']</td>\n",
-              "      <td id=\"T_87ae0_row178_col6\" class=\"data row178 col6\" >['text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row179_col0\" class=\"data row179 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
-              "      <td id=\"T_87ae0_row179_col1\" class=\"data row179 col1\" >Accuracy</td>\n",
-              "      <td id=\"T_87ae0_row179_col2\" class=\"data row179 col2\" >Calculates the accuracy of a model</td>\n",
-              "      <td id=\"T_87ae0_row179_col3\" class=\"data row179 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row179_col4\" class=\"data row179 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row179_col5\" class=\"data row179 col5\" >['classification']</td>\n",
-              "      <td id=\"T_87ae0_row179_col6\" class=\"data row179 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row180_col0\" class=\"data row180 col0\" >validmind.unit_metrics.classification.F1</td>\n",
-              "      <td id=\"T_87ae0_row180_col1\" class=\"data row180 col1\" >F1</td>\n",
-              "      <td id=\"T_87ae0_row180_col2\" class=\"data row180 col2\" >Calculates the F1 score for a classification model.</td>\n",
-              "      <td id=\"T_87ae0_row180_col3\" class=\"data row180 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row180_col4\" class=\"data row180 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row180_col5\" class=\"data row180 col5\" >['classification']</td>\n",
-              "      <td id=\"T_87ae0_row180_col6\" class=\"data row180 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row181_col0\" class=\"data row181 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
-              "      <td id=\"T_87ae0_row181_col1\" class=\"data row181 col1\" >Precision</td>\n",
-              "      <td id=\"T_87ae0_row181_col2\" class=\"data row181 col2\" >Calculates the precision for a classification model.</td>\n",
-              "      <td id=\"T_87ae0_row181_col3\" class=\"data row181 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row181_col4\" class=\"data row181 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row181_col5\" class=\"data row181 col5\" >['classification']</td>\n",
-              "      <td id=\"T_87ae0_row181_col6\" class=\"data row181 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row182_col0\" class=\"data row182 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
-              "      <td id=\"T_87ae0_row182_col1\" class=\"data row182 col1\" >ROC AUC</td>\n",
-              "      <td id=\"T_87ae0_row182_col2\" class=\"data row182 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
-              "      <td id=\"T_87ae0_row182_col3\" class=\"data row182 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row182_col4\" class=\"data row182 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row182_col5\" class=\"data row182 col5\" >['classification']</td>\n",
-              "      <td id=\"T_87ae0_row182_col6\" class=\"data row182 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row183_col0\" class=\"data row183 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
-              "      <td id=\"T_87ae0_row183_col1\" class=\"data row183 col1\" >Recall</td>\n",
-              "      <td id=\"T_87ae0_row183_col2\" class=\"data row183 col2\" >Calculates the recall for a classification model.</td>\n",
-              "      <td id=\"T_87ae0_row183_col3\" class=\"data row183 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row183_col4\" class=\"data row183 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row183_col5\" class=\"data row183 col5\" >['classification']</td>\n",
-              "      <td id=\"T_87ae0_row183_col6\" class=\"data row183 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row184_col0\" class=\"data row184 col0\" >validmind.unit_metrics.regression.AdjustedRSquaredScore</td>\n",
-              "      <td id=\"T_87ae0_row184_col1\" class=\"data row184 col1\" >Adjusted R Squared Score</td>\n",
-              "      <td id=\"T_87ae0_row184_col2\" class=\"data row184 col2\" >Calculates the adjusted R-squared score for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row184_col3\" class=\"data row184 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row184_col4\" class=\"data row184 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row184_col5\" class=\"data row184 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row184_col6\" class=\"data row184 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row185_col0\" class=\"data row185 col0\" >validmind.unit_metrics.regression.GiniCoefficient</td>\n",
-              "      <td id=\"T_87ae0_row185_col1\" class=\"data row185 col1\" >Gini Coefficient</td>\n",
-              "      <td id=\"T_87ae0_row185_col2\" class=\"data row185 col2\" >Calculates the Gini coefficient for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row185_col3\" class=\"data row185 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row185_col4\" class=\"data row185 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row185_col5\" class=\"data row185 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row185_col6\" class=\"data row185 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row186_col0\" class=\"data row186 col0\" >validmind.unit_metrics.regression.HuberLoss</td>\n",
-              "      <td id=\"T_87ae0_row186_col1\" class=\"data row186 col1\" >Huber Loss</td>\n",
-              "      <td id=\"T_87ae0_row186_col2\" class=\"data row186 col2\" >Calculates the Huber loss for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row186_col3\" class=\"data row186 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row186_col4\" class=\"data row186 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row186_col5\" class=\"data row186 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row186_col6\" class=\"data row186 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row187_col0\" class=\"data row187 col0\" >validmind.unit_metrics.regression.KolmogorovSmirnovStatistic</td>\n",
-              "      <td id=\"T_87ae0_row187_col1\" class=\"data row187 col1\" >Kolmogorov Smirnov Statistic</td>\n",
-              "      <td id=\"T_87ae0_row187_col2\" class=\"data row187 col2\" >Calculates the Kolmogorov-Smirnov statistic for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row187_col3\" class=\"data row187 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_87ae0_row187_col4\" class=\"data row187 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row187_col5\" class=\"data row187 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row187_col6\" class=\"data row187 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row188_col0\" class=\"data row188 col0\" >validmind.unit_metrics.regression.MeanAbsoluteError</td>\n",
-              "      <td id=\"T_87ae0_row188_col1\" class=\"data row188 col1\" >Mean Absolute Error</td>\n",
-              "      <td id=\"T_87ae0_row188_col2\" class=\"data row188 col2\" >Calculates the mean absolute error for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row188_col3\" class=\"data row188 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row188_col4\" class=\"data row188 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row188_col5\" class=\"data row188 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row188_col6\" class=\"data row188 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row189_col0\" class=\"data row189 col0\" >validmind.unit_metrics.regression.MeanAbsolutePercentageError</td>\n",
-              "      <td id=\"T_87ae0_row189_col1\" class=\"data row189 col1\" >Mean Absolute Percentage Error</td>\n",
-              "      <td id=\"T_87ae0_row189_col2\" class=\"data row189 col2\" >Calculates the mean absolute percentage error for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row189_col3\" class=\"data row189 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row189_col4\" class=\"data row189 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row189_col5\" class=\"data row189 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row189_col6\" class=\"data row189 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row190_col0\" class=\"data row190 col0\" >validmind.unit_metrics.regression.MeanBiasDeviation</td>\n",
-              "      <td id=\"T_87ae0_row190_col1\" class=\"data row190 col1\" >Mean Bias Deviation</td>\n",
-              "      <td id=\"T_87ae0_row190_col2\" class=\"data row190 col2\" >Calculates the mean bias deviation for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row190_col3\" class=\"data row190 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row190_col4\" class=\"data row190 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row190_col5\" class=\"data row190 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row190_col6\" class=\"data row190 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row191_col0\" class=\"data row191 col0\" >validmind.unit_metrics.regression.MeanSquaredError</td>\n",
-              "      <td id=\"T_87ae0_row191_col1\" class=\"data row191 col1\" >Mean Squared Error</td>\n",
-              "      <td id=\"T_87ae0_row191_col2\" class=\"data row191 col2\" >Calculates the mean squared error for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row191_col3\" class=\"data row191 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row191_col4\" class=\"data row191 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row191_col5\" class=\"data row191 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row191_col6\" class=\"data row191 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row192_col0\" class=\"data row192 col0\" >validmind.unit_metrics.regression.QuantileLoss</td>\n",
-              "      <td id=\"T_87ae0_row192_col1\" class=\"data row192 col1\" >Quantile Loss</td>\n",
-              "      <td id=\"T_87ae0_row192_col2\" class=\"data row192 col2\" >Calculates the quantile loss for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row192_col3\" class=\"data row192 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row192_col4\" class=\"data row192 col4\" >{'quantile': {'type': '_empty', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_87ae0_row192_col5\" class=\"data row192 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row192_col6\" class=\"data row192 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row193_col0\" class=\"data row193 col0\" >validmind.unit_metrics.regression.RSquaredScore</td>\n",
-              "      <td id=\"T_87ae0_row193_col1\" class=\"data row193 col1\" >R Squared Score</td>\n",
-              "      <td id=\"T_87ae0_row193_col2\" class=\"data row193 col2\" >Calculates the R-squared score for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row193_col3\" class=\"data row193 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row193_col4\" class=\"data row193 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row193_col5\" class=\"data row193 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row193_col6\" class=\"data row193 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_87ae0_row194_col0\" class=\"data row194 col0\" >validmind.unit_metrics.regression.RootMeanSquaredError</td>\n",
-              "      <td id=\"T_87ae0_row194_col1\" class=\"data row194 col1\" >Root Mean Squared Error</td>\n",
-              "      <td id=\"T_87ae0_row194_col2\" class=\"data row194 col2\" >Calculates the root mean squared error for a regression model.</td>\n",
-              "      <td id=\"T_87ae0_row194_col3\" class=\"data row194 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_87ae0_row194_col4\" class=\"data row194 col4\" >{}</td>\n",
-              "      <td id=\"T_87ae0_row194_col5\" class=\"data row194 col5\" >['regression']</td>\n",
-              "      <td id=\"T_87ae0_row194_col6\" class=\"data row194 col6\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.ACFandPACFPlot</td>\n",
+              "      <td id=\"T_0502a_row0_col1\" class=\"data row0 col1\" >AC Fand PACF Plot</td>\n",
+              "      <td id=\"T_0502a_row0_col2\" class=\"data row0 col2\" >Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...</td>\n",
+              "      <td id=\"T_0502a_row0_col3\" class=\"data row0 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row0_col4\" class=\"data row0 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row0_col5\" class=\"data row0 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row0_col6\" class=\"data row0 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row0_col7\" class=\"data row0 col7\" >['time_series_data', 'forecasting', 'statistical_test', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row0_col8\" class=\"data row0 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ADF</td>\n",
+              "      <td id=\"T_0502a_row1_col1\" class=\"data row1 col1\" >ADF</td>\n",
+              "      <td id=\"T_0502a_row1_col2\" class=\"data row1 col2\" >Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....</td>\n",
+              "      <td id=\"T_0502a_row1_col3\" class=\"data row1 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row1_col4\" class=\"data row1 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row1_col5\" class=\"data row1 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row1_col6\" class=\"data row1 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row1_col7\" class=\"data row1 col7\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity']</td>\n",
+              "      <td id=\"T_0502a_row1_col8\" class=\"data row1 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.AutoAR</td>\n",
+              "      <td id=\"T_0502a_row2_col1\" class=\"data row2 col1\" >Auto AR</td>\n",
+              "      <td id=\"T_0502a_row2_col2\" class=\"data row2 col2\" >Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....</td>\n",
+              "      <td id=\"T_0502a_row2_col3\" class=\"data row2 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row2_col4\" class=\"data row2 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row2_col5\" class=\"data row2 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row2_col6\" class=\"data row2 col6\" >{'max_ar_order': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_0502a_row2_col7\" class=\"data row2 col7\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
+              "      <td id=\"T_0502a_row2_col8\" class=\"data row2 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.AutoMA</td>\n",
+              "      <td id=\"T_0502a_row3_col1\" class=\"data row3 col1\" >Auto MA</td>\n",
+              "      <td id=\"T_0502a_row3_col2\" class=\"data row3 col2\" >Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...</td>\n",
+              "      <td id=\"T_0502a_row3_col3\" class=\"data row3 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row3_col4\" class=\"data row3 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row3_col5\" class=\"data row3 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row3_col6\" class=\"data row3 col6\" >{'max_ma_order': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_0502a_row3_col7\" class=\"data row3 col7\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
+              "      <td id=\"T_0502a_row3_col8\" class=\"data row3 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.AutoStationarity</td>\n",
+              "      <td id=\"T_0502a_row4_col1\" class=\"data row4 col1\" >Auto Stationarity</td>\n",
+              "      <td id=\"T_0502a_row4_col2\" class=\"data row4 col2\" >Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....</td>\n",
+              "      <td id=\"T_0502a_row4_col3\" class=\"data row4 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row4_col4\" class=\"data row4 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row4_col5\" class=\"data row4 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row4_col6\" class=\"data row4 col6\" >{'max_order': {'type': 'int', 'default': 5}, 'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_0502a_row4_col7\" class=\"data row4 col7\" >['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']</td>\n",
+              "      <td id=\"T_0502a_row4_col8\" class=\"data row4 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
+              "      <td id=\"T_0502a_row5_col1\" class=\"data row5 col1\" >Bivariate Scatter Plots</td>\n",
+              "      <td id=\"T_0502a_row5_col2\" class=\"data row5 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
+              "      <td id=\"T_0502a_row5_col3\" class=\"data row5 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row5_col4\" class=\"data row5 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row5_col5\" class=\"data row5 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row5_col6\" class=\"data row5 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row5_col7\" class=\"data row5 col7\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row5_col8\" class=\"data row5 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.BoxPierce</td>\n",
+              "      <td id=\"T_0502a_row6_col1\" class=\"data row6 col1\" >Box Pierce</td>\n",
+              "      <td id=\"T_0502a_row6_col2\" class=\"data row6 col2\" >Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....</td>\n",
+              "      <td id=\"T_0502a_row6_col3\" class=\"data row6 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row6_col4\" class=\"data row6 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row6_col5\" class=\"data row6 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row6_col6\" class=\"data row6 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row6_col7\" class=\"data row6 col7\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row6_col8\" class=\"data row6 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
+              "      <td id=\"T_0502a_row7_col1\" class=\"data row7 col1\" >Chi Squared Features Table</td>\n",
+              "      <td id=\"T_0502a_row7_col2\" class=\"data row7 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
+              "      <td id=\"T_0502a_row7_col3\" class=\"data row7 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row7_col4\" class=\"data row7 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row7_col5\" class=\"data row7 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row7_col6\" class=\"data row7 col6\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_0502a_row7_col7\" class=\"data row7 col7\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
+              "      <td id=\"T_0502a_row7_col8\" class=\"data row7 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.ClassImbalance</td>\n",
+              "      <td id=\"T_0502a_row8_col1\" class=\"data row8 col1\" >Class Imbalance</td>\n",
+              "      <td id=\"T_0502a_row8_col2\" class=\"data row8 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
+              "      <td id=\"T_0502a_row8_col3\" class=\"data row8 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row8_col4\" class=\"data row8 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row8_col5\" class=\"data row8 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row8_col6\" class=\"data row8 col6\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_0502a_row8_col7\" class=\"data row8 col7\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
+              "      <td id=\"T_0502a_row8_col8\" class=\"data row8 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.DatasetDescription</td>\n",
+              "      <td id=\"T_0502a_row9_col1\" class=\"data row9 col1\" >Dataset Description</td>\n",
+              "      <td id=\"T_0502a_row9_col2\" class=\"data row9 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
+              "      <td id=\"T_0502a_row9_col3\" class=\"data row9 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row9_col4\" class=\"data row9 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row9_col5\" class=\"data row9 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row9_col6\" class=\"data row9 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row9_col7\" class=\"data row9 col7\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
+              "      <td id=\"T_0502a_row9_col8\" class=\"data row9 col8\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.DatasetSplit</td>\n",
+              "      <td id=\"T_0502a_row10_col1\" class=\"data row10 col1\" >Dataset Split</td>\n",
+              "      <td id=\"T_0502a_row10_col2\" class=\"data row10 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
+              "      <td id=\"T_0502a_row10_col3\" class=\"data row10 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row10_col4\" class=\"data row10 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row10_col5\" class=\"data row10 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_0502a_row10_col6\" class=\"data row10 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row10_col7\" class=\"data row10 col7\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
+              "      <td id=\"T_0502a_row10_col8\" class=\"data row10 col8\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
+              "      <td id=\"T_0502a_row11_col1\" class=\"data row11 col1\" >Descriptive Statistics</td>\n",
+              "      <td id=\"T_0502a_row11_col2\" class=\"data row11 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
+              "      <td id=\"T_0502a_row11_col3\" class=\"data row11 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row11_col4\" class=\"data row11 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row11_col5\" class=\"data row11 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row11_col6\" class=\"data row11 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row11_col7\" class=\"data row11 col7\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
+              "      <td id=\"T_0502a_row11_col8\" class=\"data row11 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.DickeyFullerGLS</td>\n",
+              "      <td id=\"T_0502a_row12_col1\" class=\"data row12 col1\" >Dickey Fuller GLS</td>\n",
+              "      <td id=\"T_0502a_row12_col2\" class=\"data row12 col2\" >Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration....</td>\n",
+              "      <td id=\"T_0502a_row12_col3\" class=\"data row12 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row12_col4\" class=\"data row12 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row12_col5\" class=\"data row12 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row12_col6\" class=\"data row12 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row12_col7\" class=\"data row12 col7\" >['time_series_data', 'forecasting', 'unit_root_test']</td>\n",
+              "      <td id=\"T_0502a_row12_col8\" class=\"data row12 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.Duplicates</td>\n",
+              "      <td id=\"T_0502a_row13_col1\" class=\"data row13 col1\" >Duplicates</td>\n",
+              "      <td id=\"T_0502a_row13_col2\" class=\"data row13 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
+              "      <td id=\"T_0502a_row13_col3\" class=\"data row13 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row13_col4\" class=\"data row13 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row13_col5\" class=\"data row13 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row13_col6\" class=\"data row13 col6\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
+              "      <td id=\"T_0502a_row13_col7\" class=\"data row13 col7\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
+              "      <td id=\"T_0502a_row13_col8\" class=\"data row13 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.EngleGrangerCoint</td>\n",
+              "      <td id=\"T_0502a_row14_col1\" class=\"data row14 col1\" >Engle Granger Coint</td>\n",
+              "      <td id=\"T_0502a_row14_col2\" class=\"data row14 col2\" >Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test....</td>\n",
+              "      <td id=\"T_0502a_row14_col3\" class=\"data row14 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row14_col4\" class=\"data row14 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row14_col5\" class=\"data row14 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row14_col6\" class=\"data row14 col6\" >{'threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_0502a_row14_col7\" class=\"data row14 col7\" >['time_series_data', 'statistical_test', 'forecasting']</td>\n",
+              "      <td id=\"T_0502a_row14_col8\" class=\"data row14 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
+              "      <td id=\"T_0502a_row15_col1\" class=\"data row15 col1\" >Feature Target Correlation Plot</td>\n",
+              "      <td id=\"T_0502a_row15_col2\" class=\"data row15 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
+              "      <td id=\"T_0502a_row15_col3\" class=\"data row15 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row15_col4\" class=\"data row15 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row15_col5\" class=\"data row15 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row15_col6\" class=\"data row15 col6\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
+              "      <td id=\"T_0502a_row15_col7\" class=\"data row15 col7\" >['tabular_data', 'visualization', 'correlation']</td>\n",
+              "      <td id=\"T_0502a_row15_col8\" class=\"data row15 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.HighCardinality</td>\n",
+              "      <td id=\"T_0502a_row16_col1\" class=\"data row16 col1\" >High Cardinality</td>\n",
+              "      <td id=\"T_0502a_row16_col2\" class=\"data row16 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
+              "      <td id=\"T_0502a_row16_col3\" class=\"data row16 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row16_col4\" class=\"data row16 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row16_col5\" class=\"data row16 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row16_col6\" class=\"data row16 col6\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
+              "      <td id=\"T_0502a_row16_col7\" class=\"data row16 col7\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
+              "      <td id=\"T_0502a_row16_col8\" class=\"data row16 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
+              "      <td id=\"T_0502a_row17_col1\" class=\"data row17 col1\" >High Pearson Correlation</td>\n",
+              "      <td id=\"T_0502a_row17_col2\" class=\"data row17 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
+              "      <td id=\"T_0502a_row17_col3\" class=\"data row17 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row17_col4\" class=\"data row17 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row17_col5\" class=\"data row17 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row17_col6\" class=\"data row17 col6\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row17_col7\" class=\"data row17 col7\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
+              "      <td id=\"T_0502a_row17_col8\" class=\"data row17 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
+              "      <td id=\"T_0502a_row18_col1\" class=\"data row18 col1\" >IQR Outliers Bar Plot</td>\n",
+              "      <td id=\"T_0502a_row18_col2\" class=\"data row18 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
+              "      <td id=\"T_0502a_row18_col3\" class=\"data row18 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row18_col4\" class=\"data row18 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row18_col5\" class=\"data row18 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row18_col6\" class=\"data row18 col6\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
+              "      <td id=\"T_0502a_row18_col7\" class=\"data row18 col7\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
+              "      <td id=\"T_0502a_row18_col8\" class=\"data row18 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
+              "      <td id=\"T_0502a_row19_col1\" class=\"data row19 col1\" >IQR Outliers Table</td>\n",
+              "      <td id=\"T_0502a_row19_col2\" class=\"data row19 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
+              "      <td id=\"T_0502a_row19_col3\" class=\"data row19 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row19_col4\" class=\"data row19 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row19_col5\" class=\"data row19 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row19_col6\" class=\"data row19 col6\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
+              "      <td id=\"T_0502a_row19_col7\" class=\"data row19 col7\" >['tabular_data', 'numerical_data']</td>\n",
+              "      <td id=\"T_0502a_row19_col8\" class=\"data row19 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
+              "      <td id=\"T_0502a_row20_col1\" class=\"data row20 col1\" >Isolation Forest Outliers</td>\n",
+              "      <td id=\"T_0502a_row20_col2\" class=\"data row20 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
+              "      <td id=\"T_0502a_row20_col3\" class=\"data row20 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row20_col4\" class=\"data row20 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row20_col5\" class=\"data row20 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row20_col6\" class=\"data row20 col6\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row20_col7\" class=\"data row20 col7\" >['tabular_data', 'anomaly_detection']</td>\n",
+              "      <td id=\"T_0502a_row20_col8\" class=\"data row20 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.JarqueBera</td>\n",
+              "      <td id=\"T_0502a_row21_col1\" class=\"data row21 col1\" >Jarque Bera</td>\n",
+              "      <td id=\"T_0502a_row21_col2\" class=\"data row21 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
+              "      <td id=\"T_0502a_row21_col3\" class=\"data row21 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row21_col4\" class=\"data row21 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row21_col5\" class=\"data row21 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row21_col6\" class=\"data row21 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row21_col7\" class=\"data row21 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row21_col8\" class=\"data row21 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.KPSS</td>\n",
+              "      <td id=\"T_0502a_row22_col1\" class=\"data row22 col1\" >KPSS</td>\n",
+              "      <td id=\"T_0502a_row22_col2\" class=\"data row22 col2\" >Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test....</td>\n",
+              "      <td id=\"T_0502a_row22_col3\" class=\"data row22 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row22_col4\" class=\"data row22 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row22_col5\" class=\"data row22 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row22_col6\" class=\"data row22 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row22_col7\" class=\"data row22 col7\" >['time_series_data', 'stationarity', 'unit_root_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row22_col8\" class=\"data row22 col8\" >['data_validation']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.LJungBox</td>\n",
+              "      <td id=\"T_0502a_row23_col1\" class=\"data row23 col1\" >L Jung Box</td>\n",
+              "      <td id=\"T_0502a_row23_col2\" class=\"data row23 col2\" >Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....</td>\n",
+              "      <td id=\"T_0502a_row23_col3\" class=\"data row23 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row23_col4\" class=\"data row23 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row23_col5\" class=\"data row23 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row23_col6\" class=\"data row23 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row23_col7\" class=\"data row23 col7\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row23_col8\" class=\"data row23 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.LaggedCorrelationHeatmap</td>\n",
+              "      <td id=\"T_0502a_row24_col1\" class=\"data row24 col1\" >Lagged Correlation Heatmap</td>\n",
+              "      <td id=\"T_0502a_row24_col2\" class=\"data row24 col2\" >Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...</td>\n",
+              "      <td id=\"T_0502a_row24_col3\" class=\"data row24 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row24_col4\" class=\"data row24 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row24_col5\" class=\"data row24 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row24_col6\" class=\"data row24 col6\" >{'num_lags': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_0502a_row24_col7\" class=\"data row24 col7\" >['time_series_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row24_col8\" class=\"data row24 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.MissingValues</td>\n",
+              "      <td id=\"T_0502a_row25_col1\" class=\"data row25 col1\" >Missing Values</td>\n",
+              "      <td id=\"T_0502a_row25_col2\" class=\"data row25 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
+              "      <td id=\"T_0502a_row25_col3\" class=\"data row25 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row25_col4\" class=\"data row25 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row25_col5\" class=\"data row25 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row25_col6\" class=\"data row25 col6\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
+              "      <td id=\"T_0502a_row25_col7\" class=\"data row25 col7\" >['tabular_data', 'data_quality']</td>\n",
+              "      <td id=\"T_0502a_row25_col8\" class=\"data row25 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
+              "      <td id=\"T_0502a_row26_col1\" class=\"data row26 col1\" >Missing Values Bar Plot</td>\n",
+              "      <td id=\"T_0502a_row26_col2\" class=\"data row26 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
+              "      <td id=\"T_0502a_row26_col3\" class=\"data row26 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row26_col4\" class=\"data row26 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row26_col5\" class=\"data row26 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row26_col6\" class=\"data row26 col6\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
+              "      <td id=\"T_0502a_row26_col7\" class=\"data row26 col7\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row26_col8\" class=\"data row26 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.MutualInformation</td>\n",
+              "      <td id=\"T_0502a_row27_col1\" class=\"data row27 col1\" >Mutual Information</td>\n",
+              "      <td id=\"T_0502a_row27_col2\" class=\"data row27 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
+              "      <td id=\"T_0502a_row27_col3\" class=\"data row27 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row27_col4\" class=\"data row27 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row27_col5\" class=\"data row27 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row27_col6\" class=\"data row27 col6\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
+              "      <td id=\"T_0502a_row27_col7\" class=\"data row27 col7\" >['feature_selection', 'data_analysis']</td>\n",
+              "      <td id=\"T_0502a_row27_col8\" class=\"data row27 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
+              "      <td id=\"T_0502a_row28_col1\" class=\"data row28 col1\" >Pearson Correlation Matrix</td>\n",
+              "      <td id=\"T_0502a_row28_col2\" class=\"data row28 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
+              "      <td id=\"T_0502a_row28_col3\" class=\"data row28 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row28_col4\" class=\"data row28 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row28_col5\" class=\"data row28 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row28_col6\" class=\"data row28 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row28_col7\" class=\"data row28 col7\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
+              "      <td id=\"T_0502a_row28_col8\" class=\"data row28 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.PhillipsPerronArch</td>\n",
+              "      <td id=\"T_0502a_row29_col1\" class=\"data row29 col1\" >Phillips Perron Arch</td>\n",
+              "      <td id=\"T_0502a_row29_col2\" class=\"data row29 col2\" >Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test....</td>\n",
+              "      <td id=\"T_0502a_row29_col3\" class=\"data row29 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row29_col4\" class=\"data row29 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row29_col5\" class=\"data row29 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row29_col6\" class=\"data row29 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row29_col7\" class=\"data row29 col7\" >['time_series_data', 'forecasting', 'statistical_test', 'unit_root_test']</td>\n",
+              "      <td id=\"T_0502a_row29_col8\" class=\"data row29 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
+              "      <td id=\"T_0502a_row30_col1\" class=\"data row30 col1\" >Protected Classes Description</td>\n",
+              "      <td id=\"T_0502a_row30_col2\" class=\"data row30 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
+              "      <td id=\"T_0502a_row30_col3\" class=\"data row30 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row30_col4\" class=\"data row30 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row30_col5\" class=\"data row30 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row30_col6\" class=\"data row30 col6\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row30_col7\" class=\"data row30 col7\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
+              "      <td id=\"T_0502a_row30_col8\" class=\"data row30 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.RollingStatsPlot</td>\n",
+              "      <td id=\"T_0502a_row31_col1\" class=\"data row31 col1\" >Rolling Stats Plot</td>\n",
+              "      <td id=\"T_0502a_row31_col2\" class=\"data row31 col2\" >Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified...</td>\n",
+              "      <td id=\"T_0502a_row31_col3\" class=\"data row31 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row31_col4\" class=\"data row31 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row31_col5\" class=\"data row31 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row31_col6\" class=\"data row31 col6\" >{'window_size': {'type': 'int', 'default': 12}}</td>\n",
+              "      <td id=\"T_0502a_row31_col7\" class=\"data row31 col7\" >['time_series_data', 'visualization', 'stationarity']</td>\n",
+              "      <td id=\"T_0502a_row31_col8\" class=\"data row31 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.RunsTest</td>\n",
+              "      <td id=\"T_0502a_row32_col1\" class=\"data row32 col1\" >Runs Test</td>\n",
+              "      <td id=\"T_0502a_row32_col2\" class=\"data row32 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
+              "      <td id=\"T_0502a_row32_col3\" class=\"data row32 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row32_col4\" class=\"data row32 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row32_col5\" class=\"data row32 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row32_col6\" class=\"data row32 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row32_col7\" class=\"data row32 col7\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row32_col8\" class=\"data row32 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row33_col0\" class=\"data row33 col0\" >validmind.data_validation.ScatterPlot</td>\n",
+              "      <td id=\"T_0502a_row33_col1\" class=\"data row33 col1\" >Scatter Plot</td>\n",
+              "      <td id=\"T_0502a_row33_col2\" class=\"data row33 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
+              "      <td id=\"T_0502a_row33_col3\" class=\"data row33 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row33_col4\" class=\"data row33 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row33_col5\" class=\"data row33 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row33_col6\" class=\"data row33 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row33_col7\" class=\"data row33 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row33_col8\" class=\"data row33 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row34_col0\" class=\"data row34 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
+              "      <td id=\"T_0502a_row34_col1\" class=\"data row34 col1\" >Score Band Default Rates</td>\n",
+              "      <td id=\"T_0502a_row34_col2\" class=\"data row34 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
+              "      <td id=\"T_0502a_row34_col3\" class=\"data row34 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row34_col4\" class=\"data row34 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row34_col5\" class=\"data row34 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row34_col6\" class=\"data row34 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row34_col7\" class=\"data row34 col7\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
+              "      <td id=\"T_0502a_row34_col8\" class=\"data row34 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row35_col0\" class=\"data row35 col0\" >validmind.data_validation.SeasonalDecompose</td>\n",
+              "      <td id=\"T_0502a_row35_col1\" class=\"data row35 col1\" >Seasonal Decompose</td>\n",
+              "      <td id=\"T_0502a_row35_col2\" class=\"data row35 col2\" >Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components....</td>\n",
+              "      <td id=\"T_0502a_row35_col3\" class=\"data row35 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row35_col4\" class=\"data row35 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row35_col5\" class=\"data row35 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row35_col6\" class=\"data row35 col6\" >{'seasonal_model': {'type': 'str', 'default': 'additive'}}</td>\n",
+              "      <td id=\"T_0502a_row35_col7\" class=\"data row35 col7\" >['time_series_data', 'seasonality', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row35_col8\" class=\"data row35 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row36_col0\" class=\"data row36 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
+              "      <td id=\"T_0502a_row36_col1\" class=\"data row36 col1\" >Shapiro Wilk</td>\n",
+              "      <td id=\"T_0502a_row36_col2\" class=\"data row36 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
+              "      <td id=\"T_0502a_row36_col3\" class=\"data row36 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row36_col4\" class=\"data row36 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row36_col5\" class=\"data row36 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row36_col6\" class=\"data row36 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row36_col7\" class=\"data row36 col7\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
+              "      <td id=\"T_0502a_row36_col8\" class=\"data row36 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row37_col0\" class=\"data row37 col0\" >validmind.data_validation.Skewness</td>\n",
+              "      <td id=\"T_0502a_row37_col1\" class=\"data row37 col1\" >Skewness</td>\n",
+              "      <td id=\"T_0502a_row37_col2\" class=\"data row37 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
+              "      <td id=\"T_0502a_row37_col3\" class=\"data row37 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row37_col4\" class=\"data row37 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row37_col5\" class=\"data row37 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row37_col6\" class=\"data row37 col6\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
+              "      <td id=\"T_0502a_row37_col7\" class=\"data row37 col7\" >['data_quality', 'tabular_data']</td>\n",
+              "      <td id=\"T_0502a_row37_col8\" class=\"data row37 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row38_col0\" class=\"data row38 col0\" >validmind.data_validation.SpreadPlot</td>\n",
+              "      <td id=\"T_0502a_row38_col1\" class=\"data row38 col1\" >Spread Plot</td>\n",
+              "      <td id=\"T_0502a_row38_col2\" class=\"data row38 col2\" >Assesses potential correlations between pairs of time series variables through visualization to enhance...</td>\n",
+              "      <td id=\"T_0502a_row38_col3\" class=\"data row38 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row38_col4\" class=\"data row38 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row38_col5\" class=\"data row38 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row38_col6\" class=\"data row38 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row38_col7\" class=\"data row38 col7\" >['time_series_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row38_col8\" class=\"data row38 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row39_col0\" class=\"data row39 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
+              "      <td id=\"T_0502a_row39_col1\" class=\"data row39 col1\" >Tabular Categorical Bar Plots</td>\n",
+              "      <td id=\"T_0502a_row39_col2\" class=\"data row39 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
+              "      <td id=\"T_0502a_row39_col3\" class=\"data row39 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row39_col4\" class=\"data row39 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row39_col5\" class=\"data row39 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row39_col6\" class=\"data row39 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row39_col7\" class=\"data row39 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row39_col8\" class=\"data row39 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row40_col0\" class=\"data row40 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
+              "      <td id=\"T_0502a_row40_col1\" class=\"data row40 col1\" >Tabular Date Time Histograms</td>\n",
+              "      <td id=\"T_0502a_row40_col2\" class=\"data row40 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
+              "      <td id=\"T_0502a_row40_col3\" class=\"data row40 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row40_col4\" class=\"data row40 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row40_col5\" class=\"data row40 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row40_col6\" class=\"data row40 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row40_col7\" class=\"data row40 col7\" >['time_series_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row40_col8\" class=\"data row40 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row41_col0\" class=\"data row41 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
+              "      <td id=\"T_0502a_row41_col1\" class=\"data row41 col1\" >Tabular Description Tables</td>\n",
+              "      <td id=\"T_0502a_row41_col2\" class=\"data row41 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
+              "      <td id=\"T_0502a_row41_col3\" class=\"data row41 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row41_col4\" class=\"data row41 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row41_col5\" class=\"data row41 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row41_col6\" class=\"data row41 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row41_col7\" class=\"data row41 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_0502a_row41_col8\" class=\"data row41 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row42_col0\" class=\"data row42 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
+              "      <td id=\"T_0502a_row42_col1\" class=\"data row42 col1\" >Tabular Numerical Histograms</td>\n",
+              "      <td id=\"T_0502a_row42_col2\" class=\"data row42 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
+              "      <td id=\"T_0502a_row42_col3\" class=\"data row42 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row42_col4\" class=\"data row42 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row42_col5\" class=\"data row42 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row42_col6\" class=\"data row42 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row42_col7\" class=\"data row42 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row42_col8\" class=\"data row42 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row43_col0\" class=\"data row43 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
+              "      <td id=\"T_0502a_row43_col1\" class=\"data row43 col1\" >Target Rate Bar Plots</td>\n",
+              "      <td id=\"T_0502a_row43_col2\" class=\"data row43 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
+              "      <td id=\"T_0502a_row43_col3\" class=\"data row43 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row43_col4\" class=\"data row43 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row43_col5\" class=\"data row43 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row43_col6\" class=\"data row43 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row43_col7\" class=\"data row43 col7\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
+              "      <td id=\"T_0502a_row43_col8\" class=\"data row43 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row44_col0\" class=\"data row44 col0\" >validmind.data_validation.TimeSeriesDescription</td>\n",
+              "      <td id=\"T_0502a_row44_col1\" class=\"data row44 col1\" >Time Series Description</td>\n",
+              "      <td id=\"T_0502a_row44_col2\" class=\"data row44 col2\" >Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,...</td>\n",
+              "      <td id=\"T_0502a_row44_col3\" class=\"data row44 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row44_col4\" class=\"data row44 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row44_col5\" class=\"data row44 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row44_col6\" class=\"data row44 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row44_col7\" class=\"data row44 col7\" >['time_series_data', 'analysis']</td>\n",
+              "      <td id=\"T_0502a_row44_col8\" class=\"data row44 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row45_col0\" class=\"data row45 col0\" >validmind.data_validation.TimeSeriesDescriptiveStatistics</td>\n",
+              "      <td id=\"T_0502a_row45_col1\" class=\"data row45 col1\" >Time Series Descriptive Statistics</td>\n",
+              "      <td id=\"T_0502a_row45_col2\" class=\"data row45 col2\" >Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues....</td>\n",
+              "      <td id=\"T_0502a_row45_col3\" class=\"data row45 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row45_col4\" class=\"data row45 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row45_col5\" class=\"data row45 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row45_col6\" class=\"data row45 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row45_col7\" class=\"data row45 col7\" >['time_series_data', 'analysis']</td>\n",
+              "      <td id=\"T_0502a_row45_col8\" class=\"data row45 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row46_col0\" class=\"data row46 col0\" >validmind.data_validation.TimeSeriesFrequency</td>\n",
+              "      <td id=\"T_0502a_row46_col1\" class=\"data row46 col1\" >Time Series Frequency</td>\n",
+              "      <td id=\"T_0502a_row46_col2\" class=\"data row46 col2\" >Evaluates consistency of time series data frequency and generates a frequency plot....</td>\n",
+              "      <td id=\"T_0502a_row46_col3\" class=\"data row46 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row46_col4\" class=\"data row46 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row46_col5\" class=\"data row46 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row46_col6\" class=\"data row46 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row46_col7\" class=\"data row46 col7\" >['time_series_data']</td>\n",
+              "      <td id=\"T_0502a_row46_col8\" class=\"data row46 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row47_col0\" class=\"data row47 col0\" >validmind.data_validation.TimeSeriesHistogram</td>\n",
+              "      <td id=\"T_0502a_row47_col1\" class=\"data row47 col1\" >Time Series Histogram</td>\n",
+              "      <td id=\"T_0502a_row47_col2\" class=\"data row47 col2\" >Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....</td>\n",
+              "      <td id=\"T_0502a_row47_col3\" class=\"data row47 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row47_col4\" class=\"data row47 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row47_col5\" class=\"data row47 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row47_col6\" class=\"data row47 col6\" >{'nbins': {'type': '_empty', 'default': 30}}</td>\n",
+              "      <td id=\"T_0502a_row47_col7\" class=\"data row47 col7\" >['data_validation', 'visualization', 'time_series_data']</td>\n",
+              "      <td id=\"T_0502a_row47_col8\" class=\"data row47 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row48_col0\" class=\"data row48 col0\" >validmind.data_validation.TimeSeriesLinePlot</td>\n",
+              "      <td id=\"T_0502a_row48_col1\" class=\"data row48 col1\" >Time Series Line Plot</td>\n",
+              "      <td id=\"T_0502a_row48_col2\" class=\"data row48 col2\" >Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....</td>\n",
+              "      <td id=\"T_0502a_row48_col3\" class=\"data row48 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row48_col4\" class=\"data row48 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row48_col5\" class=\"data row48 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row48_col6\" class=\"data row48 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row48_col7\" class=\"data row48 col7\" >['time_series_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row48_col8\" class=\"data row48 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row49_col0\" class=\"data row49 col0\" >validmind.data_validation.TimeSeriesMissingValues</td>\n",
+              "      <td id=\"T_0502a_row49_col1\" class=\"data row49 col1\" >Time Series Missing Values</td>\n",
+              "      <td id=\"T_0502a_row49_col2\" class=\"data row49 col2\" >Validates time-series data quality by confirming the count of missing values is below a certain threshold....</td>\n",
+              "      <td id=\"T_0502a_row49_col3\" class=\"data row49 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row49_col4\" class=\"data row49 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row49_col5\" class=\"data row49 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row49_col6\" class=\"data row49 col6\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
+              "      <td id=\"T_0502a_row49_col7\" class=\"data row49 col7\" >['time_series_data']</td>\n",
+              "      <td id=\"T_0502a_row49_col8\" class=\"data row49 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row50_col0\" class=\"data row50 col0\" >validmind.data_validation.TimeSeriesOutliers</td>\n",
+              "      <td id=\"T_0502a_row50_col1\" class=\"data row50 col1\" >Time Series Outliers</td>\n",
+              "      <td id=\"T_0502a_row50_col2\" class=\"data row50 col2\" >Identifies and visualizes outliers in time-series data using the z-score method....</td>\n",
+              "      <td id=\"T_0502a_row50_col3\" class=\"data row50 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row50_col4\" class=\"data row50 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row50_col5\" class=\"data row50 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row50_col6\" class=\"data row50 col6\" >{'zscore_threshold': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_0502a_row50_col7\" class=\"data row50 col7\" >['time_series_data']</td>\n",
+              "      <td id=\"T_0502a_row50_col8\" class=\"data row50 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row51_col0\" class=\"data row51 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
+              "      <td id=\"T_0502a_row51_col1\" class=\"data row51 col1\" >Too Many Zero Values</td>\n",
+              "      <td id=\"T_0502a_row51_col2\" class=\"data row51 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
+              "      <td id=\"T_0502a_row51_col3\" class=\"data row51 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row51_col4\" class=\"data row51 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row51_col5\" class=\"data row51 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row51_col6\" class=\"data row51 col6\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
+              "      <td id=\"T_0502a_row51_col7\" class=\"data row51 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_0502a_row51_col8\" class=\"data row51 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row52_col0\" class=\"data row52 col0\" >validmind.data_validation.UniqueRows</td>\n",
+              "      <td id=\"T_0502a_row52_col1\" class=\"data row52 col1\" >Unique Rows</td>\n",
+              "      <td id=\"T_0502a_row52_col2\" class=\"data row52 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
+              "      <td id=\"T_0502a_row52_col3\" class=\"data row52 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row52_col4\" class=\"data row52 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row52_col5\" class=\"data row52 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row52_col6\" class=\"data row52 col6\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
+              "      <td id=\"T_0502a_row52_col7\" class=\"data row52 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_0502a_row52_col8\" class=\"data row52 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row53_col0\" class=\"data row53 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
+              "      <td id=\"T_0502a_row53_col1\" class=\"data row53 col1\" >WOE Bin Plots</td>\n",
+              "      <td id=\"T_0502a_row53_col2\" class=\"data row53 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
+              "      <td id=\"T_0502a_row53_col3\" class=\"data row53 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row53_col4\" class=\"data row53 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row53_col5\" class=\"data row53 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row53_col6\" class=\"data row53 col6\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
+              "      <td id=\"T_0502a_row53_col7\" class=\"data row53 col7\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
+              "      <td id=\"T_0502a_row53_col8\" class=\"data row53 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row54_col0\" class=\"data row54 col0\" >validmind.data_validation.WOEBinTable</td>\n",
+              "      <td id=\"T_0502a_row54_col1\" class=\"data row54 col1\" >WOE Bin Table</td>\n",
+              "      <td id=\"T_0502a_row54_col2\" class=\"data row54 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
+              "      <td id=\"T_0502a_row54_col3\" class=\"data row54 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row54_col4\" class=\"data row54 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row54_col5\" class=\"data row54 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row54_col6\" class=\"data row54 col6\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row54_col7\" class=\"data row54 col7\" >['tabular_data', 'categorical_data']</td>\n",
+              "      <td id=\"T_0502a_row54_col8\" class=\"data row54 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row55_col0\" class=\"data row55 col0\" >validmind.data_validation.ZivotAndrewsArch</td>\n",
+              "      <td id=\"T_0502a_row55_col1\" class=\"data row55 col1\" >Zivot Andrews Arch</td>\n",
+              "      <td id=\"T_0502a_row55_col2\" class=\"data row55 col2\" >Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test....</td>\n",
+              "      <td id=\"T_0502a_row55_col3\" class=\"data row55 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row55_col4\" class=\"data row55 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row55_col5\" class=\"data row55 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row55_col6\" class=\"data row55 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row55_col7\" class=\"data row55 col7\" >['time_series_data', 'stationarity', 'unit_root_test']</td>\n",
+              "      <td id=\"T_0502a_row55_col8\" class=\"data row55 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row56_col0\" class=\"data row56 col0\" >validmind.data_validation.nlp.CommonWords</td>\n",
+              "      <td id=\"T_0502a_row56_col1\" class=\"data row56 col1\" >Common Words</td>\n",
+              "      <td id=\"T_0502a_row56_col2\" class=\"data row56 col2\" >Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns....</td>\n",
+              "      <td id=\"T_0502a_row56_col3\" class=\"data row56 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row56_col4\" class=\"data row56 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row56_col5\" class=\"data row56 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row56_col6\" class=\"data row56 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row56_col7\" class=\"data row56 col7\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
+              "      <td id=\"T_0502a_row56_col8\" class=\"data row56 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row57_col0\" class=\"data row57 col0\" >validmind.data_validation.nlp.Hashtags</td>\n",
+              "      <td id=\"T_0502a_row57_col1\" class=\"data row57 col1\" >Hashtags</td>\n",
+              "      <td id=\"T_0502a_row57_col2\" class=\"data row57 col2\" >Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....</td>\n",
+              "      <td id=\"T_0502a_row57_col3\" class=\"data row57 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row57_col4\" class=\"data row57 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row57_col5\" class=\"data row57 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row57_col6\" class=\"data row57 col6\" >{'top_hashtags': {'type': 'int', 'default': 25}}</td>\n",
+              "      <td id=\"T_0502a_row57_col7\" class=\"data row57 col7\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
+              "      <td id=\"T_0502a_row57_col8\" class=\"data row57 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row58_col0\" class=\"data row58 col0\" >validmind.data_validation.nlp.LanguageDetection</td>\n",
+              "      <td id=\"T_0502a_row58_col1\" class=\"data row58 col1\" >Language Detection</td>\n",
+              "      <td id=\"T_0502a_row58_col2\" class=\"data row58 col2\" >Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages....</td>\n",
+              "      <td id=\"T_0502a_row58_col3\" class=\"data row58 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row58_col4\" class=\"data row58 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row58_col5\" class=\"data row58 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row58_col6\" class=\"data row58 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row58_col7\" class=\"data row58 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row58_col8\" class=\"data row58 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row59_col0\" class=\"data row59 col0\" >validmind.data_validation.nlp.Mentions</td>\n",
+              "      <td id=\"T_0502a_row59_col1\" class=\"data row59 col1\" >Mentions</td>\n",
+              "      <td id=\"T_0502a_row59_col2\" class=\"data row59 col2\" >Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....</td>\n",
+              "      <td id=\"T_0502a_row59_col3\" class=\"data row59 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row59_col4\" class=\"data row59 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row59_col5\" class=\"data row59 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row59_col6\" class=\"data row59 col6\" >{'top_mentions': {'type': 'int', 'default': 25}}</td>\n",
+              "      <td id=\"T_0502a_row59_col7\" class=\"data row59 col7\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
+              "      <td id=\"T_0502a_row59_col8\" class=\"data row59 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row60_col0\" class=\"data row60 col0\" >validmind.data_validation.nlp.PolarityAndSubjectivity</td>\n",
+              "      <td id=\"T_0502a_row60_col1\" class=\"data row60 col1\" >Polarity And Subjectivity</td>\n",
+              "      <td id=\"T_0502a_row60_col2\" class=\"data row60 col2\" >Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution....</td>\n",
+              "      <td id=\"T_0502a_row60_col3\" class=\"data row60 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row60_col4\" class=\"data row60 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row60_col5\" class=\"data row60 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row60_col6\" class=\"data row60 col6\" >{'threshold_subjectivity': {'type': '_empty', 'default': 0.5}, 'threshold_polarity': {'type': '_empty', 'default': 0}}</td>\n",
+              "      <td id=\"T_0502a_row60_col7\" class=\"data row60 col7\" >['nlp', 'text_data', 'data_validation']</td>\n",
+              "      <td id=\"T_0502a_row60_col8\" class=\"data row60 col8\" >['nlp']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row61_col0\" class=\"data row61 col0\" >validmind.data_validation.nlp.Punctuations</td>\n",
+              "      <td id=\"T_0502a_row61_col1\" class=\"data row61 col1\" >Punctuations</td>\n",
+              "      <td id=\"T_0502a_row61_col2\" class=\"data row61 col2\" >Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....</td>\n",
+              "      <td id=\"T_0502a_row61_col3\" class=\"data row61 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row61_col4\" class=\"data row61 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row61_col5\" class=\"data row61 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row61_col6\" class=\"data row61 col6\" >{'count_mode': {'type': '_empty', 'default': 'token'}}</td>\n",
+              "      <td id=\"T_0502a_row61_col7\" class=\"data row61 col7\" >['nlp', 'text_data', 'visualization', 'frequency_analysis']</td>\n",
+              "      <td id=\"T_0502a_row61_col8\" class=\"data row61 col8\" >['text_classification', 'text_summarization', 'nlp']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row62_col0\" class=\"data row62 col0\" >validmind.data_validation.nlp.Sentiment</td>\n",
+              "      <td id=\"T_0502a_row62_col1\" class=\"data row62 col1\" >Sentiment</td>\n",
+              "      <td id=\"T_0502a_row62_col2\" class=\"data row62 col2\" >Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....</td>\n",
+              "      <td id=\"T_0502a_row62_col3\" class=\"data row62 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row62_col4\" class=\"data row62 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row62_col5\" class=\"data row62 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row62_col6\" class=\"data row62 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row62_col7\" class=\"data row62 col7\" >['nlp', 'text_data', 'data_validation']</td>\n",
+              "      <td id=\"T_0502a_row62_col8\" class=\"data row62 col8\" >['nlp']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row63_col0\" class=\"data row63 col0\" >validmind.data_validation.nlp.StopWords</td>\n",
+              "      <td id=\"T_0502a_row63_col1\" class=\"data row63 col1\" >Stop Words</td>\n",
+              "      <td id=\"T_0502a_row63_col2\" class=\"data row63 col2\" >Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....</td>\n",
+              "      <td id=\"T_0502a_row63_col3\" class=\"data row63 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row63_col4\" class=\"data row63 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row63_col5\" class=\"data row63 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row63_col6\" class=\"data row63 col6\" >{'min_percent_threshold': {'type': 'float', 'default': 0.5}, 'num_words': {'type': 'int', 'default': 25}}</td>\n",
+              "      <td id=\"T_0502a_row63_col7\" class=\"data row63 col7\" >['nlp', 'text_data', 'frequency_analysis', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row63_col8\" class=\"data row63 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row64_col0\" class=\"data row64 col0\" >validmind.data_validation.nlp.TextDescription</td>\n",
+              "      <td id=\"T_0502a_row64_col1\" class=\"data row64 col1\" >Text Description</td>\n",
+              "      <td id=\"T_0502a_row64_col2\" class=\"data row64 col2\" >Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate...</td>\n",
+              "      <td id=\"T_0502a_row64_col3\" class=\"data row64 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row64_col4\" class=\"data row64 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row64_col5\" class=\"data row64 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row64_col6\" class=\"data row64 col6\" >{'unwanted_tokens': {'type': 'set', 'default': {'s', 'mrs', 'us', \"''\", ' ', 'ms', 'dr', 'dollar', '``', 'mr', \"'s\", \"s'\"}}, 'lang': {'type': 'str', 'default': 'english'}}</td>\n",
+              "      <td id=\"T_0502a_row64_col7\" class=\"data row64 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row64_col8\" class=\"data row64 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row65_col0\" class=\"data row65 col0\" >validmind.data_validation.nlp.Toxicity</td>\n",
+              "      <td id=\"T_0502a_row65_col1\" class=\"data row65 col1\" >Toxicity</td>\n",
+              "      <td id=\"T_0502a_row65_col2\" class=\"data row65 col2\" >Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores....</td>\n",
+              "      <td id=\"T_0502a_row65_col3\" class=\"data row65 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row65_col4\" class=\"data row65 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row65_col5\" class=\"data row65 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row65_col6\" class=\"data row65 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row65_col7\" class=\"data row65 col7\" >['nlp', 'text_data', 'data_validation']</td>\n",
+              "      <td id=\"T_0502a_row65_col8\" class=\"data row65 col8\" >['nlp']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row66_col0\" class=\"data row66 col0\" >validmind.model_validation.BertScore</td>\n",
+              "      <td id=\"T_0502a_row66_col1\" class=\"data row66 col1\" >Bert Score</td>\n",
+              "      <td id=\"T_0502a_row66_col2\" class=\"data row66 col2\" >Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms...</td>\n",
+              "      <td id=\"T_0502a_row66_col3\" class=\"data row66 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row66_col4\" class=\"data row66 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row66_col5\" class=\"data row66 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row66_col6\" class=\"data row66 col6\" >{'evaluation_model': {'type': '_empty', 'default': 'distilbert-base-uncased'}}</td>\n",
+              "      <td id=\"T_0502a_row66_col7\" class=\"data row66 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row66_col8\" class=\"data row66 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row67_col0\" class=\"data row67 col0\" >validmind.model_validation.BleuScore</td>\n",
+              "      <td id=\"T_0502a_row67_col1\" class=\"data row67 col1\" >Bleu Score</td>\n",
+              "      <td id=\"T_0502a_row67_col2\" class=\"data row67 col2\" >Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...</td>\n",
+              "      <td id=\"T_0502a_row67_col3\" class=\"data row67 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row67_col4\" class=\"data row67 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row67_col5\" class=\"data row67 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row67_col6\" class=\"data row67 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row67_col7\" class=\"data row67 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row67_col8\" class=\"data row67 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row68_col0\" class=\"data row68 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
+              "      <td id=\"T_0502a_row68_col1\" class=\"data row68 col1\" >Cluster Size Distribution</td>\n",
+              "      <td id=\"T_0502a_row68_col2\" class=\"data row68 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
+              "      <td id=\"T_0502a_row68_col3\" class=\"data row68 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row68_col4\" class=\"data row68 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row68_col5\" class=\"data row68 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row68_col6\" class=\"data row68 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row68_col7\" class=\"data row68 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row68_col8\" class=\"data row68 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row69_col0\" class=\"data row69 col0\" >validmind.model_validation.ContextualRecall</td>\n",
+              "      <td id=\"T_0502a_row69_col1\" class=\"data row69 col1\" >Contextual Recall</td>\n",
+              "      <td id=\"T_0502a_row69_col2\" class=\"data row69 col2\" >Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct...</td>\n",
+              "      <td id=\"T_0502a_row69_col3\" class=\"data row69 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row69_col4\" class=\"data row69 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row69_col5\" class=\"data row69 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row69_col6\" class=\"data row69 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row69_col7\" class=\"data row69 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row69_col8\" class=\"data row69 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row70_col0\" class=\"data row70 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
+              "      <td id=\"T_0502a_row70_col1\" class=\"data row70 col1\" >Features AUC</td>\n",
+              "      <td id=\"T_0502a_row70_col2\" class=\"data row70 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
+              "      <td id=\"T_0502a_row70_col3\" class=\"data row70 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row70_col4\" class=\"data row70 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row70_col5\" class=\"data row70 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row70_col6\" class=\"data row70 col6\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
+              "      <td id=\"T_0502a_row70_col7\" class=\"data row70 col7\" >['feature_importance', 'AUC', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row70_col8\" class=\"data row70 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row71_col0\" class=\"data row71 col0\" >validmind.model_validation.MeteorScore</td>\n",
+              "      <td id=\"T_0502a_row71_col1\" class=\"data row71 col1\" >Meteor Score</td>\n",
+              "      <td id=\"T_0502a_row71_col2\" class=\"data row71 col2\" >Assesses the quality of machine-generated translations by comparing them to human-produced references using the...</td>\n",
+              "      <td id=\"T_0502a_row71_col3\" class=\"data row71 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row71_col4\" class=\"data row71 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row71_col5\" class=\"data row71 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row71_col6\" class=\"data row71 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row71_col7\" class=\"data row71 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row71_col8\" class=\"data row71 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row72_col0\" class=\"data row72 col0\" >validmind.model_validation.ModelMetadata</td>\n",
+              "      <td id=\"T_0502a_row72_col1\" class=\"data row72 col1\" >Model Metadata</td>\n",
+              "      <td id=\"T_0502a_row72_col2\" class=\"data row72 col2\" >Compare metadata of different models and generate a summary table with the results....</td>\n",
+              "      <td id=\"T_0502a_row72_col3\" class=\"data row72 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row72_col4\" class=\"data row72 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row72_col5\" class=\"data row72 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row72_col6\" class=\"data row72 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row72_col7\" class=\"data row72 col7\" >['model_training', 'metadata']</td>\n",
+              "      <td id=\"T_0502a_row72_col8\" class=\"data row72 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row73_col0\" class=\"data row73 col0\" >validmind.model_validation.ModelPredictionResiduals</td>\n",
+              "      <td id=\"T_0502a_row73_col1\" class=\"data row73 col1\" >Model Prediction Residuals</td>\n",
+              "      <td id=\"T_0502a_row73_col2\" class=\"data row73 col2\" >Assesses normality and behavior of residuals in regression models through visualization and statistical tests....</td>\n",
+              "      <td id=\"T_0502a_row73_col3\" class=\"data row73 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row73_col4\" class=\"data row73 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row73_col5\" class=\"data row73 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row73_col6\" class=\"data row73 col6\" >{'nbins': {'type': 'int', 'default': 100}, 'p_value_threshold': {'type': 'float', 'default': 0.05}, 'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row73_col7\" class=\"data row73 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row73_col8\" class=\"data row73 col8\" >['residual_analysis', 'visualization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row74_col0\" class=\"data row74 col0\" >validmind.model_validation.RegardScore</td>\n",
+              "      <td id=\"T_0502a_row74_col1\" class=\"data row74 col1\" >Regard Score</td>\n",
+              "      <td id=\"T_0502a_row74_col2\" class=\"data row74 col2\" >Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard...</td>\n",
+              "      <td id=\"T_0502a_row74_col3\" class=\"data row74 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row74_col4\" class=\"data row74 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row74_col5\" class=\"data row74 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row74_col6\" class=\"data row74 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row74_col7\" class=\"data row74 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row74_col8\" class=\"data row74 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row75_col0\" class=\"data row75 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
+              "      <td id=\"T_0502a_row75_col1\" class=\"data row75 col1\" >Regression Residuals Plot</td>\n",
+              "      <td id=\"T_0502a_row75_col2\" class=\"data row75 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
+              "      <td id=\"T_0502a_row75_col3\" class=\"data row75 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row75_col4\" class=\"data row75 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row75_col5\" class=\"data row75 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row75_col6\" class=\"data row75 col6\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_0502a_row75_col7\" class=\"data row75 col7\" >['model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row75_col8\" class=\"data row75 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row76_col0\" class=\"data row76 col0\" >validmind.model_validation.RougeScore</td>\n",
+              "      <td id=\"T_0502a_row76_col1\" class=\"data row76 col1\" >Rouge Score</td>\n",
+              "      <td id=\"T_0502a_row76_col2\" class=\"data row76 col2\" >Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide...</td>\n",
+              "      <td id=\"T_0502a_row76_col3\" class=\"data row76 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row76_col4\" class=\"data row76 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row76_col5\" class=\"data row76 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row76_col6\" class=\"data row76 col6\" >{'metric': {'type': 'str', 'default': 'rouge-1'}}</td>\n",
+              "      <td id=\"T_0502a_row76_col7\" class=\"data row76 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row76_col8\" class=\"data row76 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row77_col0\" class=\"data row77 col0\" >validmind.model_validation.TimeSeriesPredictionWithCI</td>\n",
+              "      <td id=\"T_0502a_row77_col1\" class=\"data row77 col1\" >Time Series Prediction With CI</td>\n",
+              "      <td id=\"T_0502a_row77_col2\" class=\"data row77 col2\" >Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence...</td>\n",
+              "      <td id=\"T_0502a_row77_col3\" class=\"data row77 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row77_col4\" class=\"data row77 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row77_col5\" class=\"data row77 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row77_col6\" class=\"data row77 col6\" >{'confidence': {'type': 'float', 'default': 0.95}}</td>\n",
+              "      <td id=\"T_0502a_row77_col7\" class=\"data row77 col7\" >['model_predictions', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row77_col8\" class=\"data row77 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row78_col0\" class=\"data row78 col0\" >validmind.model_validation.TimeSeriesPredictionsPlot</td>\n",
+              "      <td id=\"T_0502a_row78_col1\" class=\"data row78 col1\" >Time Series Predictions Plot</td>\n",
+              "      <td id=\"T_0502a_row78_col2\" class=\"data row78 col2\" >Plot actual vs predicted values for time series data and generate a visual comparison for the model....</td>\n",
+              "      <td id=\"T_0502a_row78_col3\" class=\"data row78 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row78_col4\" class=\"data row78 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row78_col5\" class=\"data row78 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row78_col6\" class=\"data row78 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row78_col7\" class=\"data row78 col7\" >['model_predictions', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row78_col8\" class=\"data row78 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row79_col0\" class=\"data row79 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
+              "      <td id=\"T_0502a_row79_col1\" class=\"data row79 col1\" >Time Series R2 Square By Segments</td>\n",
+              "      <td id=\"T_0502a_row79_col2\" class=\"data row79 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
+              "      <td id=\"T_0502a_row79_col3\" class=\"data row79 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row79_col4\" class=\"data row79 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row79_col5\" class=\"data row79 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row79_col6\" class=\"data row79 col6\" >{'segments': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row79_col7\" class=\"data row79 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_0502a_row79_col8\" class=\"data row79 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row80_col0\" class=\"data row80 col0\" >validmind.model_validation.TokenDisparity</td>\n",
+              "      <td id=\"T_0502a_row80_col1\" class=\"data row80 col1\" >Token Disparity</td>\n",
+              "      <td id=\"T_0502a_row80_col2\" class=\"data row80 col2\" >Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and...</td>\n",
+              "      <td id=\"T_0502a_row80_col3\" class=\"data row80 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row80_col4\" class=\"data row80 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row80_col5\" class=\"data row80 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row80_col6\" class=\"data row80 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row80_col7\" class=\"data row80 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row80_col8\" class=\"data row80 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row81_col0\" class=\"data row81 col0\" >validmind.model_validation.ToxicityScore</td>\n",
+              "      <td id=\"T_0502a_row81_col1\" class=\"data row81 col1\" >Toxicity Score</td>\n",
+              "      <td id=\"T_0502a_row81_col2\" class=\"data row81 col2\" >Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content....</td>\n",
+              "      <td id=\"T_0502a_row81_col3\" class=\"data row81 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row81_col4\" class=\"data row81 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row81_col5\" class=\"data row81 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row81_col6\" class=\"data row81 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row81_col7\" class=\"data row81 col7\" >['nlp', 'text_data', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row81_col8\" class=\"data row81 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row82_col0\" class=\"data row82 col0\" >validmind.model_validation.embeddings.ClusterDistribution</td>\n",
+              "      <td id=\"T_0502a_row82_col1\" class=\"data row82 col1\" >Cluster Distribution</td>\n",
+              "      <td id=\"T_0502a_row82_col2\" class=\"data row82 col2\" >Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....</td>\n",
+              "      <td id=\"T_0502a_row82_col3\" class=\"data row82 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row82_col4\" class=\"data row82 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row82_col5\" class=\"data row82 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row82_col6\" class=\"data row82 col6\" >{'num_clusters': {'type': 'int', 'default': 5}}</td>\n",
+              "      <td id=\"T_0502a_row82_col7\" class=\"data row82 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row82_col8\" class=\"data row82 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row83_col0\" class=\"data row83 col0\" >validmind.model_validation.embeddings.CosineSimilarityComparison</td>\n",
+              "      <td id=\"T_0502a_row83_col1\" class=\"data row83 col1\" >Cosine Similarity Comparison</td>\n",
+              "      <td id=\"T_0502a_row83_col2\" class=\"data row83 col2\" >Assesses the similarity between embeddings generated by different models using Cosine Similarity, providing both...</td>\n",
+              "      <td id=\"T_0502a_row83_col3\" class=\"data row83 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row83_col4\" class=\"data row83 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row83_col5\" class=\"data row83 col5\" >['dataset', 'models']</td>\n",
+              "      <td id=\"T_0502a_row83_col6\" class=\"data row83 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row83_col7\" class=\"data row83 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row83_col8\" class=\"data row83 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row84_col0\" class=\"data row84 col0\" >validmind.model_validation.embeddings.CosineSimilarityDistribution</td>\n",
+              "      <td id=\"T_0502a_row84_col1\" class=\"data row84 col1\" >Cosine Similarity Distribution</td>\n",
+              "      <td id=\"T_0502a_row84_col2\" class=\"data row84 col2\" >Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...</td>\n",
+              "      <td id=\"T_0502a_row84_col3\" class=\"data row84 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row84_col4\" class=\"data row84 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row84_col5\" class=\"data row84 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row84_col6\" class=\"data row84 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row84_col7\" class=\"data row84 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row84_col8\" class=\"data row84 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row85_col0\" class=\"data row85 col0\" >validmind.model_validation.embeddings.CosineSimilarityHeatmap</td>\n",
+              "      <td id=\"T_0502a_row85_col1\" class=\"data row85 col1\" >Cosine Similarity Heatmap</td>\n",
+              "      <td id=\"T_0502a_row85_col2\" class=\"data row85 col2\" >Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....</td>\n",
+              "      <td id=\"T_0502a_row85_col3\" class=\"data row85 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row85_col4\" class=\"data row85 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row85_col5\" class=\"data row85 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row85_col6\" class=\"data row85 col6\" >{'title': {'type': '_empty', 'default': 'Cosine Similarity Matrix'}, 'color': {'type': '_empty', 'default': 'Cosine Similarity'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
+              "      <td id=\"T_0502a_row85_col7\" class=\"data row85 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row85_col8\" class=\"data row85 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row86_col0\" class=\"data row86 col0\" >validmind.model_validation.embeddings.DescriptiveAnalytics</td>\n",
+              "      <td id=\"T_0502a_row86_col1\" class=\"data row86 col1\" >Descriptive Analytics</td>\n",
+              "      <td id=\"T_0502a_row86_col2\" class=\"data row86 col2\" >Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...</td>\n",
+              "      <td id=\"T_0502a_row86_col3\" class=\"data row86 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row86_col4\" class=\"data row86 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row86_col5\" class=\"data row86 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row86_col6\" class=\"data row86 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row86_col7\" class=\"data row86 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row86_col8\" class=\"data row86 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row87_col0\" class=\"data row87 col0\" >validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
+              "      <td id=\"T_0502a_row87_col1\" class=\"data row87 col1\" >Embeddings Visualization2 D</td>\n",
+              "      <td id=\"T_0502a_row87_col2\" class=\"data row87 col2\" >Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....</td>\n",
+              "      <td id=\"T_0502a_row87_col3\" class=\"data row87 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row87_col4\" class=\"data row87 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row87_col5\" class=\"data row87 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row87_col6\" class=\"data row87 col6\" >{'cluster_column': {'type': None, 'default': None}, 'perplexity': {'type': 'int', 'default': 30}}</td>\n",
+              "      <td id=\"T_0502a_row87_col7\" class=\"data row87 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row87_col8\" class=\"data row87 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row88_col0\" class=\"data row88 col0\" >validmind.model_validation.embeddings.EuclideanDistanceComparison</td>\n",
+              "      <td id=\"T_0502a_row88_col1\" class=\"data row88 col1\" >Euclidean Distance Comparison</td>\n",
+              "      <td id=\"T_0502a_row88_col2\" class=\"data row88 col2\" >Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights...</td>\n",
+              "      <td id=\"T_0502a_row88_col3\" class=\"data row88 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row88_col4\" class=\"data row88 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row88_col5\" class=\"data row88 col5\" >['dataset', 'models']</td>\n",
+              "      <td id=\"T_0502a_row88_col6\" class=\"data row88 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row88_col7\" class=\"data row88 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row88_col8\" class=\"data row88 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row89_col0\" class=\"data row89 col0\" >validmind.model_validation.embeddings.EuclideanDistanceHeatmap</td>\n",
+              "      <td id=\"T_0502a_row89_col1\" class=\"data row89 col1\" >Euclidean Distance Heatmap</td>\n",
+              "      <td id=\"T_0502a_row89_col2\" class=\"data row89 col2\" >Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....</td>\n",
+              "      <td id=\"T_0502a_row89_col3\" class=\"data row89 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row89_col4\" class=\"data row89 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row89_col5\" class=\"data row89 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row89_col6\" class=\"data row89 col6\" >{'title': {'type': '_empty', 'default': 'Euclidean Distance Matrix'}, 'color': {'type': '_empty', 'default': 'Euclidean Distance'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}</td>\n",
+              "      <td id=\"T_0502a_row89_col7\" class=\"data row89 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row89_col8\" class=\"data row89 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row90_col0\" class=\"data row90 col0\" >validmind.model_validation.embeddings.PCAComponentsPairwisePlots</td>\n",
+              "      <td id=\"T_0502a_row90_col1\" class=\"data row90 col1\" >PCA Components Pairwise Plots</td>\n",
+              "      <td id=\"T_0502a_row90_col2\" class=\"data row90 col2\" >Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model...</td>\n",
+              "      <td id=\"T_0502a_row90_col3\" class=\"data row90 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row90_col4\" class=\"data row90 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row90_col5\" class=\"data row90 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row90_col6\" class=\"data row90 col6\" >{'n_components': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_0502a_row90_col7\" class=\"data row90 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row90_col8\" class=\"data row90 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row91_col0\" class=\"data row91 col0\" >validmind.model_validation.embeddings.StabilityAnalysisKeyword</td>\n",
+              "      <td id=\"T_0502a_row91_col1\" class=\"data row91 col1\" >Stability Analysis Keyword</td>\n",
+              "      <td id=\"T_0502a_row91_col2\" class=\"data row91 col2\" >Evaluates robustness of embedding models to keyword swaps in the test dataset....</td>\n",
+              "      <td id=\"T_0502a_row91_col3\" class=\"data row91 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row91_col4\" class=\"data row91 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row91_col5\" class=\"data row91 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row91_col6\" class=\"data row91 col6\" >{'keyword_dict': {'type': None, 'default': None}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_0502a_row91_col7\" class=\"data row91 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row91_col8\" class=\"data row91 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row92_col0\" class=\"data row92 col0\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise</td>\n",
+              "      <td id=\"T_0502a_row92_col1\" class=\"data row92 col1\" >Stability Analysis Random Noise</td>\n",
+              "      <td id=\"T_0502a_row92_col2\" class=\"data row92 col2\" >Assesses the robustness of text embeddings models to random noise introduced via text perturbations....</td>\n",
+              "      <td id=\"T_0502a_row92_col3\" class=\"data row92 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row92_col4\" class=\"data row92 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row92_col5\" class=\"data row92 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row92_col6\" class=\"data row92 col6\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_0502a_row92_col7\" class=\"data row92 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row92_col8\" class=\"data row92 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row93_col0\" class=\"data row93 col0\" >validmind.model_validation.embeddings.StabilityAnalysisSynonyms</td>\n",
+              "      <td id=\"T_0502a_row93_col1\" class=\"data row93 col1\" >Stability Analysis Synonyms</td>\n",
+              "      <td id=\"T_0502a_row93_col2\" class=\"data row93 col2\" >Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....</td>\n",
+              "      <td id=\"T_0502a_row93_col3\" class=\"data row93 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row93_col4\" class=\"data row93 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row93_col5\" class=\"data row93 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row93_col6\" class=\"data row93 col6\" >{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_0502a_row93_col7\" class=\"data row93 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row93_col8\" class=\"data row93 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row94_col0\" class=\"data row94 col0\" >validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
+              "      <td id=\"T_0502a_row94_col1\" class=\"data row94 col1\" >Stability Analysis Translation</td>\n",
+              "      <td id=\"T_0502a_row94_col2\" class=\"data row94 col2\" >Evaluates robustness of text embeddings models to noise introduced by translating the original text to another...</td>\n",
+              "      <td id=\"T_0502a_row94_col3\" class=\"data row94 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row94_col4\" class=\"data row94 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row94_col5\" class=\"data row94 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row94_col6\" class=\"data row94 col6\" >{'source_lang': {'type': 'str', 'default': 'en'}, 'target_lang': {'type': 'str', 'default': 'fr'}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_0502a_row94_col7\" class=\"data row94 col7\" >['llm', 'text_data', 'embeddings', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row94_col8\" class=\"data row94 col8\" >['feature_extraction']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row95_col0\" class=\"data row95 col0\" >validmind.model_validation.embeddings.TSNEComponentsPairwisePlots</td>\n",
+              "      <td id=\"T_0502a_row95_col1\" class=\"data row95 col1\" >TSNE Components Pairwise Plots</td>\n",
+              "      <td id=\"T_0502a_row95_col2\" class=\"data row95 col2\" >Creates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential...</td>\n",
+              "      <td id=\"T_0502a_row95_col3\" class=\"data row95 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row95_col4\" class=\"data row95 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row95_col5\" class=\"data row95 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row95_col6\" class=\"data row95 col6\" >{'n_components': {'type': 'int', 'default': 2}, 'perplexity': {'type': 'int', 'default': 30}, 'title': {'type': 'str', 'default': 't-SNE'}}</td>\n",
+              "      <td id=\"T_0502a_row95_col7\" class=\"data row95 col7\" >['visualization', 'dimensionality_reduction', 'embeddings']</td>\n",
+              "      <td id=\"T_0502a_row95_col8\" class=\"data row95 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row96_col0\" class=\"data row96 col0\" >validmind.model_validation.ragas.AnswerCorrectness</td>\n",
+              "      <td id=\"T_0502a_row96_col1\" class=\"data row96 col1\" >Answer Correctness</td>\n",
+              "      <td id=\"T_0502a_row96_col2\" class=\"data row96 col2\" >Evaluates the correctness of answers in a dataset with respect to the provided ground...</td>\n",
+              "      <td id=\"T_0502a_row96_col3\" class=\"data row96 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row96_col4\" class=\"data row96 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row96_col5\" class=\"data row96 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row96_col6\" class=\"data row96 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row96_col7\" class=\"data row96 col7\" >['ragas', 'llm']</td>\n",
+              "      <td id=\"T_0502a_row96_col8\" class=\"data row96 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row97_col0\" class=\"data row97 col0\" >validmind.model_validation.ragas.AspectCritic</td>\n",
+              "      <td id=\"T_0502a_row97_col1\" class=\"data row97 col1\" >Aspect Critic</td>\n",
+              "      <td id=\"T_0502a_row97_col2\" class=\"data row97 col2\" >Evaluates generations against the following aspects: harmfulness, maliciousness,...</td>\n",
+              "      <td id=\"T_0502a_row97_col3\" class=\"data row97 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row97_col4\" class=\"data row97 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row97_col5\" class=\"data row97 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row97_col6\" class=\"data row97 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': None, 'default': None}, 'aspects': {'type': None, 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': None, 'default': None}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row97_col7\" class=\"data row97 col7\" >['ragas', 'llm', 'qualitative']</td>\n",
+              "      <td id=\"T_0502a_row97_col8\" class=\"data row97 col8\" >['text_summarization', 'text_generation', 'text_qa']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row98_col0\" class=\"data row98 col0\" >validmind.model_validation.ragas.ContextEntityRecall</td>\n",
+              "      <td id=\"T_0502a_row98_col1\" class=\"data row98 col1\" >Context Entity Recall</td>\n",
+              "      <td id=\"T_0502a_row98_col2\" class=\"data row98 col2\" >Evaluates the context entity recall for dataset entries and visualizes the results....</td>\n",
+              "      <td id=\"T_0502a_row98_col3\" class=\"data row98 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row98_col4\" class=\"data row98 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row98_col5\" class=\"data row98 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row98_col6\" class=\"data row98 col6\" >{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row98_col7\" class=\"data row98 col7\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
+              "      <td id=\"T_0502a_row98_col8\" class=\"data row98 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row99_col0\" class=\"data row99 col0\" >validmind.model_validation.ragas.ContextPrecision</td>\n",
+              "      <td id=\"T_0502a_row99_col1\" class=\"data row99 col1\" >Context Precision</td>\n",
+              "      <td id=\"T_0502a_row99_col2\" class=\"data row99 col2\" >Context Precision is a metric that evaluates whether all of the ground-truth...</td>\n",
+              "      <td id=\"T_0502a_row99_col3\" class=\"data row99 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row99_col4\" class=\"data row99 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row99_col5\" class=\"data row99 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row99_col6\" class=\"data row99 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row99_col7\" class=\"data row99 col7\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
+              "      <td id=\"T_0502a_row99_col8\" class=\"data row99 col8\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row100_col0\" class=\"data row100 col0\" >validmind.model_validation.ragas.ContextPrecisionWithoutReference</td>\n",
+              "      <td id=\"T_0502a_row100_col1\" class=\"data row100 col1\" >Context Precision Without Reference</td>\n",
+              "      <td id=\"T_0502a_row100_col2\" class=\"data row100 col2\" >Context Precision Without Reference is a metric used to evaluate the relevance of...</td>\n",
+              "      <td id=\"T_0502a_row100_col3\" class=\"data row100 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row100_col4\" class=\"data row100 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row100_col5\" class=\"data row100 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row100_col6\" class=\"data row100 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row100_col7\" class=\"data row100 col7\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
+              "      <td id=\"T_0502a_row100_col8\" class=\"data row100 col8\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row101_col0\" class=\"data row101 col0\" >validmind.model_validation.ragas.ContextRecall</td>\n",
+              "      <td id=\"T_0502a_row101_col1\" class=\"data row101 col1\" >Context Recall</td>\n",
+              "      <td id=\"T_0502a_row101_col2\" class=\"data row101 col2\" >Context recall measures the extent to which the retrieved context aligns with the...</td>\n",
+              "      <td id=\"T_0502a_row101_col3\" class=\"data row101 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row101_col4\" class=\"data row101 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row101_col5\" class=\"data row101 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row101_col6\" class=\"data row101 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row101_col7\" class=\"data row101 col7\" >['ragas', 'llm', 'retrieval_performance']</td>\n",
+              "      <td id=\"T_0502a_row101_col8\" class=\"data row101 col8\" >['text_qa', 'text_generation', 'text_summarization', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row102_col0\" class=\"data row102 col0\" >validmind.model_validation.ragas.Faithfulness</td>\n",
+              "      <td id=\"T_0502a_row102_col1\" class=\"data row102 col1\" >Faithfulness</td>\n",
+              "      <td id=\"T_0502a_row102_col2\" class=\"data row102 col2\" >Evaluates the faithfulness of the generated answers with respect to retrieved contexts....</td>\n",
+              "      <td id=\"T_0502a_row102_col3\" class=\"data row102 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row102_col4\" class=\"data row102 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row102_col5\" class=\"data row102 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row102_col6\" class=\"data row102 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row102_col7\" class=\"data row102 col7\" >['ragas', 'llm', 'rag_performance']</td>\n",
+              "      <td id=\"T_0502a_row102_col8\" class=\"data row102 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row103_col0\" class=\"data row103 col0\" >validmind.model_validation.ragas.NoiseSensitivity</td>\n",
+              "      <td id=\"T_0502a_row103_col1\" class=\"data row103 col1\" >Noise Sensitivity</td>\n",
+              "      <td id=\"T_0502a_row103_col2\" class=\"data row103 col2\" >Assesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it...</td>\n",
+              "      <td id=\"T_0502a_row103_col3\" class=\"data row103 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row103_col4\" class=\"data row103 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row103_col5\" class=\"data row103 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row103_col6\" class=\"data row103 col6\" >{'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'focus': {'type': 'str', 'default': 'relevant'}, 'user_input_column': {'type': 'str', 'default': 'user_input'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row103_col7\" class=\"data row103 col7\" >['ragas', 'llm', 'rag_performance']</td>\n",
+              "      <td id=\"T_0502a_row103_col8\" class=\"data row103 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row104_col0\" class=\"data row104 col0\" >validmind.model_validation.ragas.ResponseRelevancy</td>\n",
+              "      <td id=\"T_0502a_row104_col1\" class=\"data row104 col1\" >Response Relevancy</td>\n",
+              "      <td id=\"T_0502a_row104_col2\" class=\"data row104 col2\" >Assesses how pertinent the generated answer is to the given prompt....</td>\n",
+              "      <td id=\"T_0502a_row104_col3\" class=\"data row104 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row104_col4\" class=\"data row104 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row104_col5\" class=\"data row104 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row104_col6\" class=\"data row104 col6\" >{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': None}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row104_col7\" class=\"data row104 col7\" >['ragas', 'llm', 'rag_performance']</td>\n",
+              "      <td id=\"T_0502a_row104_col8\" class=\"data row104 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row105_col0\" class=\"data row105 col0\" >validmind.model_validation.ragas.SemanticSimilarity</td>\n",
+              "      <td id=\"T_0502a_row105_col1\" class=\"data row105 col1\" >Semantic Similarity</td>\n",
+              "      <td id=\"T_0502a_row105_col2\" class=\"data row105 col2\" >Calculates the semantic similarity between generated responses and ground truths...</td>\n",
+              "      <td id=\"T_0502a_row105_col3\" class=\"data row105 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row105_col4\" class=\"data row105 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row105_col5\" class=\"data row105 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row105_col6\" class=\"data row105 col6\" >{'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row105_col7\" class=\"data row105 col7\" >['ragas', 'llm']</td>\n",
+              "      <td id=\"T_0502a_row105_col8\" class=\"data row105 col8\" >['text_qa', 'text_generation', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row106_col0\" class=\"data row106 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
+              "      <td id=\"T_0502a_row106_col1\" class=\"data row106 col1\" >Adjusted Mutual Information</td>\n",
+              "      <td id=\"T_0502a_row106_col2\" class=\"data row106 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
+              "      <td id=\"T_0502a_row106_col3\" class=\"data row106 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row106_col4\" class=\"data row106 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row106_col5\" class=\"data row106 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row106_col6\" class=\"data row106 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row106_col7\" class=\"data row106 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_0502a_row106_col8\" class=\"data row106 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row107_col0\" class=\"data row107 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
+              "      <td id=\"T_0502a_row107_col1\" class=\"data row107 col1\" >Adjusted Rand Index</td>\n",
+              "      <td id=\"T_0502a_row107_col2\" class=\"data row107 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
+              "      <td id=\"T_0502a_row107_col3\" class=\"data row107 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row107_col4\" class=\"data row107 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row107_col5\" class=\"data row107 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row107_col6\" class=\"data row107 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row107_col7\" class=\"data row107 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_0502a_row107_col8\" class=\"data row107 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row108_col0\" class=\"data row108 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
+              "      <td id=\"T_0502a_row108_col1\" class=\"data row108 col1\" >Calibration Curve</td>\n",
+              "      <td id=\"T_0502a_row108_col2\" class=\"data row108 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
+              "      <td id=\"T_0502a_row108_col3\" class=\"data row108 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row108_col4\" class=\"data row108 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row108_col5\" class=\"data row108 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row108_col6\" class=\"data row108 col6\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_0502a_row108_col7\" class=\"data row108 col7\" >['sklearn', 'model_performance', 'classification']</td>\n",
+              "      <td id=\"T_0502a_row108_col8\" class=\"data row108 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row109_col0\" class=\"data row109 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
+              "      <td id=\"T_0502a_row109_col1\" class=\"data row109 col1\" >Classifier Performance</td>\n",
+              "      <td id=\"T_0502a_row109_col2\" class=\"data row109 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
+              "      <td id=\"T_0502a_row109_col3\" class=\"data row109 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row109_col4\" class=\"data row109 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row109_col5\" class=\"data row109 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row109_col6\" class=\"data row109 col6\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
+              "      <td id=\"T_0502a_row109_col7\" class=\"data row109 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row109_col8\" class=\"data row109 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row110_col0\" class=\"data row110 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
+              "      <td id=\"T_0502a_row110_col1\" class=\"data row110 col1\" >Classifier Threshold Optimization</td>\n",
+              "      <td id=\"T_0502a_row110_col2\" class=\"data row110 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
+              "      <td id=\"T_0502a_row110_col3\" class=\"data row110 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row110_col4\" class=\"data row110 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row110_col5\" class=\"data row110 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row110_col6\" class=\"data row110 col6\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row110_col7\" class=\"data row110 col7\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
+              "      <td id=\"T_0502a_row110_col8\" class=\"data row110 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row111_col0\" class=\"data row111 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
+              "      <td id=\"T_0502a_row111_col1\" class=\"data row111 col1\" >Cluster Cosine Similarity</td>\n",
+              "      <td id=\"T_0502a_row111_col2\" class=\"data row111 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
+              "      <td id=\"T_0502a_row111_col3\" class=\"data row111 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row111_col4\" class=\"data row111 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row111_col5\" class=\"data row111 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row111_col6\" class=\"data row111 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row111_col7\" class=\"data row111 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_0502a_row111_col8\" class=\"data row111 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row112_col0\" class=\"data row112 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
+              "      <td id=\"T_0502a_row112_col1\" class=\"data row112 col1\" >Cluster Performance Metrics</td>\n",
+              "      <td id=\"T_0502a_row112_col2\" class=\"data row112 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
+              "      <td id=\"T_0502a_row112_col3\" class=\"data row112 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row112_col4\" class=\"data row112 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row112_col5\" class=\"data row112 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row112_col6\" class=\"data row112 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row112_col7\" class=\"data row112 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_0502a_row112_col8\" class=\"data row112 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row113_col0\" class=\"data row113 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
+              "      <td id=\"T_0502a_row113_col1\" class=\"data row113 col1\" >Completeness Score</td>\n",
+              "      <td id=\"T_0502a_row113_col2\" class=\"data row113 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
+              "      <td id=\"T_0502a_row113_col3\" class=\"data row113 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row113_col4\" class=\"data row113 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row113_col5\" class=\"data row113 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row113_col6\" class=\"data row113 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row113_col7\" class=\"data row113 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_0502a_row113_col8\" class=\"data row113 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row114_col0\" class=\"data row114 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
+              "      <td id=\"T_0502a_row114_col1\" class=\"data row114 col1\" >Confusion Matrix</td>\n",
+              "      <td id=\"T_0502a_row114_col2\" class=\"data row114 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
+              "      <td id=\"T_0502a_row114_col3\" class=\"data row114 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row114_col4\" class=\"data row114 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row114_col5\" class=\"data row114 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row114_col6\" class=\"data row114 col6\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_0502a_row114_col7\" class=\"data row114 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row114_col8\" class=\"data row114 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row115_col0\" class=\"data row115 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
+              "      <td id=\"T_0502a_row115_col1\" class=\"data row115 col1\" >Feature Importance</td>\n",
+              "      <td id=\"T_0502a_row115_col2\" class=\"data row115 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
+              "      <td id=\"T_0502a_row115_col3\" class=\"data row115 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row115_col4\" class=\"data row115 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row115_col5\" class=\"data row115 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row115_col6\" class=\"data row115 col6\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_0502a_row115_col7\" class=\"data row115 col7\" >['model_explainability', 'sklearn']</td>\n",
+              "      <td id=\"T_0502a_row115_col8\" class=\"data row115 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row116_col0\" class=\"data row116 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
+              "      <td id=\"T_0502a_row116_col1\" class=\"data row116 col1\" >Fowlkes Mallows Score</td>\n",
+              "      <td id=\"T_0502a_row116_col2\" class=\"data row116 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
+              "      <td id=\"T_0502a_row116_col3\" class=\"data row116 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row116_col4\" class=\"data row116 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row116_col5\" class=\"data row116 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row116_col6\" class=\"data row116 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row116_col7\" class=\"data row116 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row116_col8\" class=\"data row116 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row117_col0\" class=\"data row117 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
+              "      <td id=\"T_0502a_row117_col1\" class=\"data row117 col1\" >Homogeneity Score</td>\n",
+              "      <td id=\"T_0502a_row117_col2\" class=\"data row117 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
+              "      <td id=\"T_0502a_row117_col3\" class=\"data row117 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row117_col4\" class=\"data row117 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row117_col5\" class=\"data row117 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row117_col6\" class=\"data row117 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row117_col7\" class=\"data row117 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row117_col8\" class=\"data row117 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row118_col0\" class=\"data row118 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
+              "      <td id=\"T_0502a_row118_col1\" class=\"data row118 col1\" >Hyper Parameters Tuning</td>\n",
+              "      <td id=\"T_0502a_row118_col2\" class=\"data row118 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
+              "      <td id=\"T_0502a_row118_col3\" class=\"data row118 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row118_col4\" class=\"data row118 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row118_col5\" class=\"data row118 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row118_col6\" class=\"data row118 col6\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row118_col7\" class=\"data row118 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row118_col8\" class=\"data row118 col8\" >['clustering', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row119_col0\" class=\"data row119 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
+              "      <td id=\"T_0502a_row119_col1\" class=\"data row119 col1\" >K Means Clusters Optimization</td>\n",
+              "      <td id=\"T_0502a_row119_col2\" class=\"data row119 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
+              "      <td id=\"T_0502a_row119_col3\" class=\"data row119 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row119_col4\" class=\"data row119 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row119_col5\" class=\"data row119 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row119_col6\" class=\"data row119 col6\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row119_col7\" class=\"data row119 col7\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
+              "      <td id=\"T_0502a_row119_col8\" class=\"data row119 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row120_col0\" class=\"data row120 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
+              "      <td id=\"T_0502a_row120_col1\" class=\"data row120 col1\" >Minimum Accuracy</td>\n",
+              "      <td id=\"T_0502a_row120_col2\" class=\"data row120 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_0502a_row120_col3\" class=\"data row120 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row120_col4\" class=\"data row120 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row120_col5\" class=\"data row120 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row120_col6\" class=\"data row120 col6\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_0502a_row120_col7\" class=\"data row120 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row120_col8\" class=\"data row120 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row121_col0\" class=\"data row121 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
+              "      <td id=\"T_0502a_row121_col1\" class=\"data row121 col1\" >Minimum F1 Score</td>\n",
+              "      <td id=\"T_0502a_row121_col2\" class=\"data row121 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
+              "      <td id=\"T_0502a_row121_col3\" class=\"data row121 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row121_col4\" class=\"data row121 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row121_col5\" class=\"data row121 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row121_col6\" class=\"data row121 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_0502a_row121_col7\" class=\"data row121 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row121_col8\" class=\"data row121 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row122_col0\" class=\"data row122 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
+              "      <td id=\"T_0502a_row122_col1\" class=\"data row122 col1\" >Minimum ROCAUC Score</td>\n",
+              "      <td id=\"T_0502a_row122_col2\" class=\"data row122 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_0502a_row122_col3\" class=\"data row122 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row122_col4\" class=\"data row122 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row122_col5\" class=\"data row122 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row122_col6\" class=\"data row122 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_0502a_row122_col7\" class=\"data row122 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row122_col8\" class=\"data row122 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row123_col0\" class=\"data row123 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
+              "      <td id=\"T_0502a_row123_col1\" class=\"data row123 col1\" >Model Parameters</td>\n",
+              "      <td id=\"T_0502a_row123_col2\" class=\"data row123 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
+              "      <td id=\"T_0502a_row123_col3\" class=\"data row123 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row123_col4\" class=\"data row123 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row123_col5\" class=\"data row123 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row123_col6\" class=\"data row123 col6\" >{'model_params': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row123_col7\" class=\"data row123 col7\" >['model_training', 'metadata']</td>\n",
+              "      <td id=\"T_0502a_row123_col8\" class=\"data row123 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row124_col0\" class=\"data row124 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
+              "      <td id=\"T_0502a_row124_col1\" class=\"data row124 col1\" >Models Performance Comparison</td>\n",
+              "      <td id=\"T_0502a_row124_col2\" class=\"data row124 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
+              "      <td id=\"T_0502a_row124_col3\" class=\"data row124 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row124_col4\" class=\"data row124 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row124_col5\" class=\"data row124 col5\" >['dataset', 'models']</td>\n",
+              "      <td id=\"T_0502a_row124_col6\" class=\"data row124 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row124_col7\" class=\"data row124 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
+              "      <td id=\"T_0502a_row124_col8\" class=\"data row124 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row125_col0\" class=\"data row125 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
+              "      <td id=\"T_0502a_row125_col1\" class=\"data row125 col1\" >Overfit Diagnosis</td>\n",
+              "      <td id=\"T_0502a_row125_col2\" class=\"data row125 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
+              "      <td id=\"T_0502a_row125_col3\" class=\"data row125 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row125_col4\" class=\"data row125 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row125_col5\" class=\"data row125 col5\" >['model', 'datasets']</td>\n",
+              "      <td id=\"T_0502a_row125_col6\" class=\"data row125 col6\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
+              "      <td id=\"T_0502a_row125_col7\" class=\"data row125 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
+              "      <td id=\"T_0502a_row125_col8\" class=\"data row125 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row126_col0\" class=\"data row126 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
+              "      <td id=\"T_0502a_row126_col1\" class=\"data row126 col1\" >Permutation Feature Importance</td>\n",
+              "      <td id=\"T_0502a_row126_col2\" class=\"data row126 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
+              "      <td id=\"T_0502a_row126_col3\" class=\"data row126 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row126_col4\" class=\"data row126 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row126_col5\" class=\"data row126 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row126_col6\" class=\"data row126 col6\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row126_col7\" class=\"data row126 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row126_col8\" class=\"data row126 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row127_col0\" class=\"data row127 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
+              "      <td id=\"T_0502a_row127_col1\" class=\"data row127 col1\" >Population Stability Index</td>\n",
+              "      <td id=\"T_0502a_row127_col2\" class=\"data row127 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
+              "      <td id=\"T_0502a_row127_col3\" class=\"data row127 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row127_col4\" class=\"data row127 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row127_col5\" class=\"data row127 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row127_col6\" class=\"data row127 col6\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
+              "      <td id=\"T_0502a_row127_col7\" class=\"data row127 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row127_col8\" class=\"data row127 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row128_col0\" class=\"data row128 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
+              "      <td id=\"T_0502a_row128_col1\" class=\"data row128 col1\" >Precision Recall Curve</td>\n",
+              "      <td id=\"T_0502a_row128_col2\" class=\"data row128 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
+              "      <td id=\"T_0502a_row128_col3\" class=\"data row128 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row128_col4\" class=\"data row128 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row128_col5\" class=\"data row128 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row128_col6\" class=\"data row128 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row128_col7\" class=\"data row128 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row128_col8\" class=\"data row128 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row129_col0\" class=\"data row129 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
+              "      <td id=\"T_0502a_row129_col1\" class=\"data row129 col1\" >ROC Curve</td>\n",
+              "      <td id=\"T_0502a_row129_col2\" class=\"data row129 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
+              "      <td id=\"T_0502a_row129_col3\" class=\"data row129 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row129_col4\" class=\"data row129 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row129_col5\" class=\"data row129 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row129_col6\" class=\"data row129 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row129_col7\" class=\"data row129 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row129_col8\" class=\"data row129 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row130_col0\" class=\"data row130 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
+              "      <td id=\"T_0502a_row130_col1\" class=\"data row130 col1\" >Regression Errors</td>\n",
+              "      <td id=\"T_0502a_row130_col2\" class=\"data row130 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
+              "      <td id=\"T_0502a_row130_col3\" class=\"data row130 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row130_col4\" class=\"data row130 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row130_col5\" class=\"data row130 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row130_col6\" class=\"data row130 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row130_col7\" class=\"data row130 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row130_col8\" class=\"data row130 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row131_col0\" class=\"data row131 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
+              "      <td id=\"T_0502a_row131_col1\" class=\"data row131 col1\" >Regression Errors Comparison</td>\n",
+              "      <td id=\"T_0502a_row131_col2\" class=\"data row131 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
+              "      <td id=\"T_0502a_row131_col3\" class=\"data row131 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row131_col4\" class=\"data row131 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row131_col5\" class=\"data row131 col5\" >['datasets', 'models']</td>\n",
+              "      <td id=\"T_0502a_row131_col6\" class=\"data row131 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row131_col7\" class=\"data row131 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_0502a_row131_col8\" class=\"data row131 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row132_col0\" class=\"data row132 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
+              "      <td id=\"T_0502a_row132_col1\" class=\"data row132 col1\" >Regression Performance</td>\n",
+              "      <td id=\"T_0502a_row132_col2\" class=\"data row132 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
+              "      <td id=\"T_0502a_row132_col3\" class=\"data row132 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row132_col4\" class=\"data row132 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row132_col5\" class=\"data row132 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row132_col6\" class=\"data row132 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row132_col7\" class=\"data row132 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row132_col8\" class=\"data row132 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row133_col0\" class=\"data row133 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
+              "      <td id=\"T_0502a_row133_col1\" class=\"data row133 col1\" >Regression R2 Square</td>\n",
+              "      <td id=\"T_0502a_row133_col2\" class=\"data row133 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
+              "      <td id=\"T_0502a_row133_col3\" class=\"data row133 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row133_col4\" class=\"data row133 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row133_col5\" class=\"data row133 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row133_col6\" class=\"data row133 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row133_col7\" class=\"data row133 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row133_col8\" class=\"data row133 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row134_col0\" class=\"data row134 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
+              "      <td id=\"T_0502a_row134_col1\" class=\"data row134 col1\" >Regression R2 Square Comparison</td>\n",
+              "      <td id=\"T_0502a_row134_col2\" class=\"data row134 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
+              "      <td id=\"T_0502a_row134_col3\" class=\"data row134 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row134_col4\" class=\"data row134 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row134_col5\" class=\"data row134 col5\" >['datasets', 'models']</td>\n",
+              "      <td id=\"T_0502a_row134_col6\" class=\"data row134 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row134_col7\" class=\"data row134 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_0502a_row134_col8\" class=\"data row134 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row135_col0\" class=\"data row135 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
+              "      <td id=\"T_0502a_row135_col1\" class=\"data row135 col1\" >Robustness Diagnosis</td>\n",
+              "      <td id=\"T_0502a_row135_col2\" class=\"data row135 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
+              "      <td id=\"T_0502a_row135_col3\" class=\"data row135 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row135_col4\" class=\"data row135 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row135_col5\" class=\"data row135 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row135_col6\" class=\"data row135 col6\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_0502a_row135_col7\" class=\"data row135 col7\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row135_col8\" class=\"data row135 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row136_col0\" class=\"data row136 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
+              "      <td id=\"T_0502a_row136_col1\" class=\"data row136 col1\" >SHAP Global Importance</td>\n",
+              "      <td id=\"T_0502a_row136_col2\" class=\"data row136 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
+              "      <td id=\"T_0502a_row136_col3\" class=\"data row136 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row136_col4\" class=\"data row136 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row136_col5\" class=\"data row136 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row136_col6\" class=\"data row136 col6\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row136_col7\" class=\"data row136 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row136_col8\" class=\"data row136 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row137_col0\" class=\"data row137 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
+              "      <td id=\"T_0502a_row137_col1\" class=\"data row137 col1\" >Score Probability Alignment</td>\n",
+              "      <td id=\"T_0502a_row137_col2\" class=\"data row137 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
+              "      <td id=\"T_0502a_row137_col3\" class=\"data row137 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row137_col4\" class=\"data row137 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row137_col5\" class=\"data row137 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row137_col6\" class=\"data row137 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_0502a_row137_col7\" class=\"data row137 col7\" >['visualization', 'credit_risk', 'calibration']</td>\n",
+              "      <td id=\"T_0502a_row137_col8\" class=\"data row137 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row138_col0\" class=\"data row138 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
+              "      <td id=\"T_0502a_row138_col1\" class=\"data row138 col1\" >Silhouette Plot</td>\n",
+              "      <td id=\"T_0502a_row138_col2\" class=\"data row138 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
+              "      <td id=\"T_0502a_row138_col3\" class=\"data row138 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row138_col4\" class=\"data row138 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row138_col5\" class=\"data row138 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row138_col6\" class=\"data row138 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row138_col7\" class=\"data row138 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row138_col8\" class=\"data row138 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row139_col0\" class=\"data row139 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
+              "      <td id=\"T_0502a_row139_col1\" class=\"data row139 col1\" >Training Test Degradation</td>\n",
+              "      <td id=\"T_0502a_row139_col2\" class=\"data row139 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
+              "      <td id=\"T_0502a_row139_col3\" class=\"data row139 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row139_col4\" class=\"data row139 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row139_col5\" class=\"data row139 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row139_col6\" class=\"data row139 col6\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_0502a_row139_col7\" class=\"data row139 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row139_col8\" class=\"data row139 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row140_col0\" class=\"data row140 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
+              "      <td id=\"T_0502a_row140_col1\" class=\"data row140 col1\" >V Measure</td>\n",
+              "      <td id=\"T_0502a_row140_col2\" class=\"data row140 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
+              "      <td id=\"T_0502a_row140_col3\" class=\"data row140 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row140_col4\" class=\"data row140 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row140_col5\" class=\"data row140 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row140_col6\" class=\"data row140 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row140_col7\" class=\"data row140 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row140_col8\" class=\"data row140 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row141_col0\" class=\"data row141 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
+              "      <td id=\"T_0502a_row141_col1\" class=\"data row141 col1\" >Weakspots Diagnosis</td>\n",
+              "      <td id=\"T_0502a_row141_col2\" class=\"data row141 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
+              "      <td id=\"T_0502a_row141_col3\" class=\"data row141 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row141_col4\" class=\"data row141 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row141_col5\" class=\"data row141 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row141_col6\" class=\"data row141 col6\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row141_col7\" class=\"data row141 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row141_col8\" class=\"data row141 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row142_col0\" class=\"data row142 col0\" >validmind.model_validation.statsmodels.AutoARIMA</td>\n",
+              "      <td id=\"T_0502a_row142_col1\" class=\"data row142 col1\" >Auto ARIMA</td>\n",
+              "      <td id=\"T_0502a_row142_col2\" class=\"data row142 col2\" >Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....</td>\n",
+              "      <td id=\"T_0502a_row142_col3\" class=\"data row142 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row142_col4\" class=\"data row142 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row142_col5\" class=\"data row142 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row142_col6\" class=\"data row142 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row142_col7\" class=\"data row142 col7\" >['time_series_data', 'forecasting', 'model_selection', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row142_col8\" class=\"data row142 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row143_col0\" class=\"data row143 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
+              "      <td id=\"T_0502a_row143_col1\" class=\"data row143 col1\" >Cumulative Prediction Probabilities</td>\n",
+              "      <td id=\"T_0502a_row143_col2\" class=\"data row143 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
+              "      <td id=\"T_0502a_row143_col3\" class=\"data row143 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row143_col4\" class=\"data row143 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row143_col5\" class=\"data row143 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row143_col6\" class=\"data row143 col6\" >{'title': {'type': 'str', 'default': 'Cumulative Probabilities'}}</td>\n",
+              "      <td id=\"T_0502a_row143_col7\" class=\"data row143 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_0502a_row143_col8\" class=\"data row143 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row144_col0\" class=\"data row144 col0\" >validmind.model_validation.statsmodels.DurbinWatsonTest</td>\n",
+              "      <td id=\"T_0502a_row144_col1\" class=\"data row144 col1\" >Durbin Watson Test</td>\n",
+              "      <td id=\"T_0502a_row144_col2\" class=\"data row144 col2\" >Assesses autocorrelation in time series data features using the Durbin-Watson statistic....</td>\n",
+              "      <td id=\"T_0502a_row144_col3\" class=\"data row144 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row144_col4\" class=\"data row144 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row144_col5\" class=\"data row144 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row144_col6\" class=\"data row144 col6\" >{'threshold': {'type': None, 'default': [1.5, 2.5]}}</td>\n",
+              "      <td id=\"T_0502a_row144_col7\" class=\"data row144 col7\" >['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row144_col8\" class=\"data row144 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row145_col0\" class=\"data row145 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
+              "      <td id=\"T_0502a_row145_col1\" class=\"data row145 col1\" >GINI Table</td>\n",
+              "      <td id=\"T_0502a_row145_col2\" class=\"data row145 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
+              "      <td id=\"T_0502a_row145_col3\" class=\"data row145 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row145_col4\" class=\"data row145 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row145_col5\" class=\"data row145 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row145_col6\" class=\"data row145 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row145_col7\" class=\"data row145 col7\" >['model_performance']</td>\n",
+              "      <td id=\"T_0502a_row145_col8\" class=\"data row145 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row146_col0\" class=\"data row146 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
+              "      <td id=\"T_0502a_row146_col1\" class=\"data row146 col1\" >Kolmogorov Smirnov</td>\n",
+              "      <td id=\"T_0502a_row146_col2\" class=\"data row146 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
+              "      <td id=\"T_0502a_row146_col3\" class=\"data row146 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row146_col4\" class=\"data row146 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row146_col5\" class=\"data row146 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row146_col6\" class=\"data row146 col6\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
+              "      <td id=\"T_0502a_row146_col7\" class=\"data row146 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row146_col8\" class=\"data row146 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row147_col0\" class=\"data row147 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
+              "      <td id=\"T_0502a_row147_col1\" class=\"data row147 col1\" >Lilliefors</td>\n",
+              "      <td id=\"T_0502a_row147_col2\" class=\"data row147 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
+              "      <td id=\"T_0502a_row147_col3\" class=\"data row147 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row147_col4\" class=\"data row147 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row147_col5\" class=\"data row147 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row147_col6\" class=\"data row147 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row147_col7\" class=\"data row147 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_0502a_row147_col8\" class=\"data row147 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row148_col0\" class=\"data row148 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
+              "      <td id=\"T_0502a_row148_col1\" class=\"data row148 col1\" >Prediction Probabilities Histogram</td>\n",
+              "      <td id=\"T_0502a_row148_col2\" class=\"data row148 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
+              "      <td id=\"T_0502a_row148_col3\" class=\"data row148 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row148_col4\" class=\"data row148 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row148_col5\" class=\"data row148 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row148_col6\" class=\"data row148 col6\" >{'title': {'type': 'str', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
+              "      <td id=\"T_0502a_row148_col7\" class=\"data row148 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_0502a_row148_col8\" class=\"data row148 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row149_col0\" class=\"data row149 col0\" >validmind.model_validation.statsmodels.RegressionCoeffs</td>\n",
+              "      <td id=\"T_0502a_row149_col1\" class=\"data row149 col1\" >Regression Coeffs</td>\n",
+              "      <td id=\"T_0502a_row149_col2\" class=\"data row149 col2\" >Assesses the significance and uncertainty of predictor variables in a regression model through visualization of...</td>\n",
+              "      <td id=\"T_0502a_row149_col3\" class=\"data row149 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row149_col4\" class=\"data row149 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row149_col5\" class=\"data row149 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row149_col6\" class=\"data row149 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row149_col7\" class=\"data row149 col7\" >['tabular_data', 'visualization', 'model_training']</td>\n",
+              "      <td id=\"T_0502a_row149_col8\" class=\"data row149 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row150_col0\" class=\"data row150 col0\" >validmind.model_validation.statsmodels.RegressionFeatureSignificance</td>\n",
+              "      <td id=\"T_0502a_row150_col1\" class=\"data row150 col1\" >Regression Feature Significance</td>\n",
+              "      <td id=\"T_0502a_row150_col2\" class=\"data row150 col2\" >Assesses and visualizes the statistical significance of features in a regression model....</td>\n",
+              "      <td id=\"T_0502a_row150_col3\" class=\"data row150 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row150_col4\" class=\"data row150 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row150_col5\" class=\"data row150 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row150_col6\" class=\"data row150 col6\" >{'fontsize': {'type': 'int', 'default': 10}, 'p_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_0502a_row150_col7\" class=\"data row150 col7\" >['statistical_test', 'model_interpretation', 'visualization', 'feature_importance']</td>\n",
+              "      <td id=\"T_0502a_row150_col8\" class=\"data row150 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row151_col0\" class=\"data row151 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlot</td>\n",
+              "      <td id=\"T_0502a_row151_col1\" class=\"data row151 col1\" >Regression Model Forecast Plot</td>\n",
+              "      <td id=\"T_0502a_row151_col2\" class=\"data row151 col2\" >Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over...</td>\n",
+              "      <td id=\"T_0502a_row151_col3\" class=\"data row151 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row151_col4\" class=\"data row151 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row151_col5\" class=\"data row151 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row151_col6\" class=\"data row151 col6\" >{'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row151_col7\" class=\"data row151 col7\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row151_col8\" class=\"data row151 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row152_col0\" class=\"data row152 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels</td>\n",
+              "      <td id=\"T_0502a_row152_col1\" class=\"data row152 col1\" >Regression Model Forecast Plot Levels</td>\n",
+              "      <td id=\"T_0502a_row152_col2\" class=\"data row152 col2\" >Assesses the alignment between forecasted and observed values in regression models through visual plots...</td>\n",
+              "      <td id=\"T_0502a_row152_col3\" class=\"data row152 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row152_col4\" class=\"data row152 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row152_col5\" class=\"data row152 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row152_col6\" class=\"data row152 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row152_col7\" class=\"data row152 col7\" >['time_series_data', 'forecasting', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row152_col8\" class=\"data row152 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row153_col0\" class=\"data row153 col0\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
+              "      <td id=\"T_0502a_row153_col1\" class=\"data row153 col1\" >Regression Model Sensitivity Plot</td>\n",
+              "      <td id=\"T_0502a_row153_col2\" class=\"data row153 col2\" >Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and...</td>\n",
+              "      <td id=\"T_0502a_row153_col3\" class=\"data row153 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row153_col4\" class=\"data row153 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row153_col5\" class=\"data row153 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row153_col6\" class=\"data row153 col6\" >{'shocks': {'type': None, 'default': [0.1]}, 'transformation': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row153_col7\" class=\"data row153 col7\" >['senstivity_analysis', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row153_col8\" class=\"data row153 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row154_col0\" class=\"data row154 col0\" >validmind.model_validation.statsmodels.RegressionModelSummary</td>\n",
+              "      <td id=\"T_0502a_row154_col1\" class=\"data row154 col1\" >Regression Model Summary</td>\n",
+              "      <td id=\"T_0502a_row154_col2\" class=\"data row154 col2\" >Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....</td>\n",
+              "      <td id=\"T_0502a_row154_col3\" class=\"data row154 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row154_col4\" class=\"data row154 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row154_col5\" class=\"data row154 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row154_col6\" class=\"data row154 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row154_col7\" class=\"data row154 col7\" >['model_performance', 'regression']</td>\n",
+              "      <td id=\"T_0502a_row154_col8\" class=\"data row154 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row155_col0\" class=\"data row155 col0\" >validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance</td>\n",
+              "      <td id=\"T_0502a_row155_col1\" class=\"data row155 col1\" >Regression Permutation Feature Importance</td>\n",
+              "      <td id=\"T_0502a_row155_col2\" class=\"data row155 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
+              "      <td id=\"T_0502a_row155_col3\" class=\"data row155 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row155_col4\" class=\"data row155 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row155_col5\" class=\"data row155 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row155_col6\" class=\"data row155 col6\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
+              "      <td id=\"T_0502a_row155_col7\" class=\"data row155 col7\" >['statsmodels', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row155_col8\" class=\"data row155 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row156_col0\" class=\"data row156 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
+              "      <td id=\"T_0502a_row156_col1\" class=\"data row156 col1\" >Scorecard Histogram</td>\n",
+              "      <td id=\"T_0502a_row156_col2\" class=\"data row156 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
+              "      <td id=\"T_0502a_row156_col3\" class=\"data row156 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row156_col4\" class=\"data row156 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row156_col5\" class=\"data row156 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_0502a_row156_col6\" class=\"data row156 col6\" >{'title': {'type': 'str', 'default': 'Histogram of Scores'}, 'score_column': {'type': 'str', 'default': 'score'}}</td>\n",
+              "      <td id=\"T_0502a_row156_col7\" class=\"data row156 col7\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
+              "      <td id=\"T_0502a_row156_col8\" class=\"data row156 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row157_col0\" class=\"data row157 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
+              "      <td id=\"T_0502a_row157_col1\" class=\"data row157 col1\" >Calibration Curve Drift</td>\n",
+              "      <td id=\"T_0502a_row157_col2\" class=\"data row157 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row157_col3\" class=\"data row157 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row157_col4\" class=\"data row157 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row157_col5\" class=\"data row157 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row157_col6\" class=\"data row157 col6\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row157_col7\" class=\"data row157 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row157_col8\" class=\"data row157 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row158_col0\" class=\"data row158 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
+              "      <td id=\"T_0502a_row158_col1\" class=\"data row158 col1\" >Class Discrimination Drift</td>\n",
+              "      <td id=\"T_0502a_row158_col2\" class=\"data row158 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row158_col3\" class=\"data row158 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row158_col4\" class=\"data row158 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row158_col5\" class=\"data row158 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row158_col6\" class=\"data row158 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row158_col7\" class=\"data row158 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row158_col8\" class=\"data row158 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row159_col0\" class=\"data row159 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
+              "      <td id=\"T_0502a_row159_col1\" class=\"data row159 col1\" >Class Imbalance Drift</td>\n",
+              "      <td id=\"T_0502a_row159_col2\" class=\"data row159 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row159_col3\" class=\"data row159 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row159_col4\" class=\"data row159 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row159_col5\" class=\"data row159 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_0502a_row159_col6\" class=\"data row159 col6\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
+              "      <td id=\"T_0502a_row159_col7\" class=\"data row159 col7\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
+              "      <td id=\"T_0502a_row159_col8\" class=\"data row159 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row160_col0\" class=\"data row160 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
+              "      <td id=\"T_0502a_row160_col1\" class=\"data row160 col1\" >Classification Accuracy Drift</td>\n",
+              "      <td id=\"T_0502a_row160_col2\" class=\"data row160 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row160_col3\" class=\"data row160 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row160_col4\" class=\"data row160 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row160_col5\" class=\"data row160 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row160_col6\" class=\"data row160 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row160_col7\" class=\"data row160 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row160_col8\" class=\"data row160 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row161_col0\" class=\"data row161 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
+              "      <td id=\"T_0502a_row161_col1\" class=\"data row161 col1\" >Confusion Matrix Drift</td>\n",
+              "      <td id=\"T_0502a_row161_col2\" class=\"data row161 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row161_col3\" class=\"data row161 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row161_col4\" class=\"data row161 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row161_col5\" class=\"data row161 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row161_col6\" class=\"data row161 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row161_col7\" class=\"data row161 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_0502a_row161_col8\" class=\"data row161 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row162_col0\" class=\"data row162 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
+              "      <td id=\"T_0502a_row162_col1\" class=\"data row162 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
+              "      <td id=\"T_0502a_row162_col2\" class=\"data row162 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row162_col3\" class=\"data row162 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row162_col4\" class=\"data row162 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row162_col5\" class=\"data row162 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row162_col6\" class=\"data row162 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row162_col7\" class=\"data row162 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_0502a_row162_col8\" class=\"data row162 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row163_col0\" class=\"data row163 col0\" >validmind.ongoing_monitoring.FeatureDrift</td>\n",
+              "      <td id=\"T_0502a_row163_col1\" class=\"data row163 col1\" >Feature Drift</td>\n",
+              "      <td id=\"T_0502a_row163_col2\" class=\"data row163 col2\" >Evaluates changes in feature distribution over time to identify potential model drift....</td>\n",
+              "      <td id=\"T_0502a_row163_col3\" class=\"data row163 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row163_col4\" class=\"data row163 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row163_col5\" class=\"data row163 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_0502a_row163_col6\" class=\"data row163 col6\" >{'bins': {'type': '_empty', 'default': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, 'feature_columns': {'type': '_empty', 'default': None}, 'psi_threshold': {'type': '_empty', 'default': 0.2}}</td>\n",
+              "      <td id=\"T_0502a_row163_col7\" class=\"data row163 col7\" >['visualization']</td>\n",
+              "      <td id=\"T_0502a_row163_col8\" class=\"data row163 col8\" >['monitoring']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row164_col0\" class=\"data row164 col0\" >validmind.ongoing_monitoring.PredictionAcrossEachFeature</td>\n",
+              "      <td id=\"T_0502a_row164_col1\" class=\"data row164 col1\" >Prediction Across Each Feature</td>\n",
+              "      <td id=\"T_0502a_row164_col2\" class=\"data row164 col2\" >Assesses differences in model predictions across individual features between reference and monitoring datasets...</td>\n",
+              "      <td id=\"T_0502a_row164_col3\" class=\"data row164 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row164_col4\" class=\"data row164 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row164_col5\" class=\"data row164 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row164_col6\" class=\"data row164 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row164_col7\" class=\"data row164 col7\" >['visualization']</td>\n",
+              "      <td id=\"T_0502a_row164_col8\" class=\"data row164 col8\" >['monitoring']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row165_col0\" class=\"data row165 col0\" >validmind.ongoing_monitoring.PredictionCorrelation</td>\n",
+              "      <td id=\"T_0502a_row165_col1\" class=\"data row165 col1\" >Prediction Correlation</td>\n",
+              "      <td id=\"T_0502a_row165_col2\" class=\"data row165 col2\" >Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential...</td>\n",
+              "      <td id=\"T_0502a_row165_col3\" class=\"data row165 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row165_col4\" class=\"data row165 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row165_col5\" class=\"data row165 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row165_col6\" class=\"data row165 col6\" >{'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row165_col7\" class=\"data row165 col7\" >['visualization']</td>\n",
+              "      <td id=\"T_0502a_row165_col8\" class=\"data row165 col8\" >['monitoring']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row166_col0\" class=\"data row166 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
+              "      <td id=\"T_0502a_row166_col1\" class=\"data row166 col1\" >Prediction Probabilities Histogram Drift</td>\n",
+              "      <td id=\"T_0502a_row166_col2\" class=\"data row166 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row166_col3\" class=\"data row166 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row166_col4\" class=\"data row166 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row166_col5\" class=\"data row166 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row166_col6\" class=\"data row166 col6\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_0502a_row166_col7\" class=\"data row166 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_0502a_row166_col8\" class=\"data row166 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row167_col0\" class=\"data row167 col0\" >validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures</td>\n",
+              "      <td id=\"T_0502a_row167_col1\" class=\"data row167 col1\" >Prediction Quantiles Across Features</td>\n",
+              "      <td id=\"T_0502a_row167_col2\" class=\"data row167 col2\" >Assesses differences in model prediction distributions across individual features between reference...</td>\n",
+              "      <td id=\"T_0502a_row167_col3\" class=\"data row167 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row167_col4\" class=\"data row167 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row167_col5\" class=\"data row167 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row167_col6\" class=\"data row167 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row167_col7\" class=\"data row167 col7\" >['visualization']</td>\n",
+              "      <td id=\"T_0502a_row167_col8\" class=\"data row167 col8\" >['monitoring']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row168_col0\" class=\"data row168 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
+              "      <td id=\"T_0502a_row168_col1\" class=\"data row168 col1\" >ROC Curve Drift</td>\n",
+              "      <td id=\"T_0502a_row168_col2\" class=\"data row168 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_0502a_row168_col3\" class=\"data row168 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row168_col4\" class=\"data row168 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row168_col5\" class=\"data row168 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row168_col6\" class=\"data row168 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row168_col7\" class=\"data row168 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_0502a_row168_col8\" class=\"data row168 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row169_col0\" class=\"data row169 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
+              "      <td id=\"T_0502a_row169_col1\" class=\"data row169 col1\" >Score Bands Drift</td>\n",
+              "      <td id=\"T_0502a_row169_col2\" class=\"data row169 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
+              "      <td id=\"T_0502a_row169_col3\" class=\"data row169 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row169_col4\" class=\"data row169 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row169_col5\" class=\"data row169 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row169_col6\" class=\"data row169 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_0502a_row169_col7\" class=\"data row169 col7\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
+              "      <td id=\"T_0502a_row169_col8\" class=\"data row169 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row170_col0\" class=\"data row170 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
+              "      <td id=\"T_0502a_row170_col1\" class=\"data row170 col1\" >Scorecard Histogram Drift</td>\n",
+              "      <td id=\"T_0502a_row170_col2\" class=\"data row170 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
+              "      <td id=\"T_0502a_row170_col3\" class=\"data row170 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row170_col4\" class=\"data row170 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row170_col5\" class=\"data row170 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_0502a_row170_col6\" class=\"data row170 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_0502a_row170_col7\" class=\"data row170 col7\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
+              "      <td id=\"T_0502a_row170_col8\" class=\"data row170 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row171_col0\" class=\"data row171 col0\" >validmind.ongoing_monitoring.TargetPredictionDistributionPlot</td>\n",
+              "      <td id=\"T_0502a_row171_col1\" class=\"data row171 col1\" >Target Prediction Distribution Plot</td>\n",
+              "      <td id=\"T_0502a_row171_col2\" class=\"data row171 col2\" >Assesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify...</td>\n",
+              "      <td id=\"T_0502a_row171_col3\" class=\"data row171 col3\" >True</td>\n",
+              "      <td id=\"T_0502a_row171_col4\" class=\"data row171 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row171_col5\" class=\"data row171 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_0502a_row171_col6\" class=\"data row171 col6\" >{'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_0502a_row171_col7\" class=\"data row171 col7\" >['visualization']</td>\n",
+              "      <td id=\"T_0502a_row171_col8\" class=\"data row171 col8\" >['monitoring']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row172_col0\" class=\"data row172 col0\" >validmind.prompt_validation.Bias</td>\n",
+              "      <td id=\"T_0502a_row172_col1\" class=\"data row172 col1\" >Bias</td>\n",
+              "      <td id=\"T_0502a_row172_col2\" class=\"data row172 col2\" >Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...</td>\n",
+              "      <td id=\"T_0502a_row172_col3\" class=\"data row172 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row172_col4\" class=\"data row172 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row172_col5\" class=\"data row172 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row172_col6\" class=\"data row172 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row172_col7\" class=\"data row172 col7\" >['llm', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row172_col8\" class=\"data row172 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row173_col0\" class=\"data row173 col0\" >validmind.prompt_validation.Clarity</td>\n",
+              "      <td id=\"T_0502a_row173_col1\" class=\"data row173 col1\" >Clarity</td>\n",
+              "      <td id=\"T_0502a_row173_col2\" class=\"data row173 col2\" >Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....</td>\n",
+              "      <td id=\"T_0502a_row173_col3\" class=\"data row173 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row173_col4\" class=\"data row173 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row173_col5\" class=\"data row173 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row173_col6\" class=\"data row173 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row173_col7\" class=\"data row173 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row173_col8\" class=\"data row173 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row174_col0\" class=\"data row174 col0\" >validmind.prompt_validation.Conciseness</td>\n",
+              "      <td id=\"T_0502a_row174_col1\" class=\"data row174 col1\" >Conciseness</td>\n",
+              "      <td id=\"T_0502a_row174_col2\" class=\"data row174 col2\" >Analyzes and grades the conciseness of prompts provided to a Large Language Model....</td>\n",
+              "      <td id=\"T_0502a_row174_col3\" class=\"data row174 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row174_col4\" class=\"data row174 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row174_col5\" class=\"data row174 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row174_col6\" class=\"data row174 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row174_col7\" class=\"data row174 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row174_col8\" class=\"data row174 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row175_col0\" class=\"data row175 col0\" >validmind.prompt_validation.Delimitation</td>\n",
+              "      <td id=\"T_0502a_row175_col1\" class=\"data row175 col1\" >Delimitation</td>\n",
+              "      <td id=\"T_0502a_row175_col2\" class=\"data row175 col2\" >Evaluates the proper use of delimiters in prompts provided to Large Language Models....</td>\n",
+              "      <td id=\"T_0502a_row175_col3\" class=\"data row175 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row175_col4\" class=\"data row175 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row175_col5\" class=\"data row175 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row175_col6\" class=\"data row175 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row175_col7\" class=\"data row175 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row175_col8\" class=\"data row175 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row176_col0\" class=\"data row176 col0\" >validmind.prompt_validation.NegativeInstruction</td>\n",
+              "      <td id=\"T_0502a_row176_col1\" class=\"data row176 col1\" >Negative Instruction</td>\n",
+              "      <td id=\"T_0502a_row176_col2\" class=\"data row176 col2\" >Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....</td>\n",
+              "      <td id=\"T_0502a_row176_col3\" class=\"data row176 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row176_col4\" class=\"data row176 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row176_col5\" class=\"data row176 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row176_col6\" class=\"data row176 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row176_col7\" class=\"data row176 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row176_col8\" class=\"data row176 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row177_col0\" class=\"data row177 col0\" >validmind.prompt_validation.Robustness</td>\n",
+              "      <td id=\"T_0502a_row177_col1\" class=\"data row177 col1\" >Robustness</td>\n",
+              "      <td id=\"T_0502a_row177_col2\" class=\"data row177 col2\" >Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test...</td>\n",
+              "      <td id=\"T_0502a_row177_col3\" class=\"data row177 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row177_col4\" class=\"data row177 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row177_col5\" class=\"data row177 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row177_col6\" class=\"data row177 col6\" >{'num_tests': {'type': '_empty', 'default': 10}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row177_col7\" class=\"data row177 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row177_col8\" class=\"data row177 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row178_col0\" class=\"data row178 col0\" >validmind.prompt_validation.Specificity</td>\n",
+              "      <td id=\"T_0502a_row178_col1\" class=\"data row178 col1\" >Specificity</td>\n",
+              "      <td id=\"T_0502a_row178_col2\" class=\"data row178 col2\" >Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,...</td>\n",
+              "      <td id=\"T_0502a_row178_col3\" class=\"data row178 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row178_col4\" class=\"data row178 col4\" >True</td>\n",
+              "      <td id=\"T_0502a_row178_col5\" class=\"data row178 col5\" >['model']</td>\n",
+              "      <td id=\"T_0502a_row178_col6\" class=\"data row178 col6\" >{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_0502a_row178_col7\" class=\"data row178 col7\" >['llm', 'zero_shot', 'few_shot']</td>\n",
+              "      <td id=\"T_0502a_row178_col8\" class=\"data row178 col8\" >['text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row179_col0\" class=\"data row179 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
+              "      <td id=\"T_0502a_row179_col1\" class=\"data row179 col1\" >Accuracy</td>\n",
+              "      <td id=\"T_0502a_row179_col2\" class=\"data row179 col2\" >Calculates the accuracy of a model</td>\n",
+              "      <td id=\"T_0502a_row179_col3\" class=\"data row179 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row179_col4\" class=\"data row179 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row179_col5\" class=\"data row179 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row179_col6\" class=\"data row179 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row179_col7\" class=\"data row179 col7\" >['classification']</td>\n",
+              "      <td id=\"T_0502a_row179_col8\" class=\"data row179 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row180_col0\" class=\"data row180 col0\" >validmind.unit_metrics.classification.F1</td>\n",
+              "      <td id=\"T_0502a_row180_col1\" class=\"data row180 col1\" >F1</td>\n",
+              "      <td id=\"T_0502a_row180_col2\" class=\"data row180 col2\" >Calculates the F1 score for a classification model.</td>\n",
+              "      <td id=\"T_0502a_row180_col3\" class=\"data row180 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row180_col4\" class=\"data row180 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row180_col5\" class=\"data row180 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row180_col6\" class=\"data row180 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row180_col7\" class=\"data row180 col7\" >['classification']</td>\n",
+              "      <td id=\"T_0502a_row180_col8\" class=\"data row180 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row181_col0\" class=\"data row181 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
+              "      <td id=\"T_0502a_row181_col1\" class=\"data row181 col1\" >Precision</td>\n",
+              "      <td id=\"T_0502a_row181_col2\" class=\"data row181 col2\" >Calculates the precision for a classification model.</td>\n",
+              "      <td id=\"T_0502a_row181_col3\" class=\"data row181 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row181_col4\" class=\"data row181 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row181_col5\" class=\"data row181 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row181_col6\" class=\"data row181 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row181_col7\" class=\"data row181 col7\" >['classification']</td>\n",
+              "      <td id=\"T_0502a_row181_col8\" class=\"data row181 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row182_col0\" class=\"data row182 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
+              "      <td id=\"T_0502a_row182_col1\" class=\"data row182 col1\" >ROC AUC</td>\n",
+              "      <td id=\"T_0502a_row182_col2\" class=\"data row182 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
+              "      <td id=\"T_0502a_row182_col3\" class=\"data row182 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row182_col4\" class=\"data row182 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row182_col5\" class=\"data row182 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row182_col6\" class=\"data row182 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row182_col7\" class=\"data row182 col7\" >['classification']</td>\n",
+              "      <td id=\"T_0502a_row182_col8\" class=\"data row182 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row183_col0\" class=\"data row183 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
+              "      <td id=\"T_0502a_row183_col1\" class=\"data row183 col1\" >Recall</td>\n",
+              "      <td id=\"T_0502a_row183_col2\" class=\"data row183 col2\" >Calculates the recall for a classification model.</td>\n",
+              "      <td id=\"T_0502a_row183_col3\" class=\"data row183 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row183_col4\" class=\"data row183 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row183_col5\" class=\"data row183 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row183_col6\" class=\"data row183 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row183_col7\" class=\"data row183 col7\" >['classification']</td>\n",
+              "      <td id=\"T_0502a_row183_col8\" class=\"data row183 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row184_col0\" class=\"data row184 col0\" >validmind.unit_metrics.regression.AdjustedRSquaredScore</td>\n",
+              "      <td id=\"T_0502a_row184_col1\" class=\"data row184 col1\" >Adjusted R Squared Score</td>\n",
+              "      <td id=\"T_0502a_row184_col2\" class=\"data row184 col2\" >Calculates the adjusted R-squared score for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row184_col3\" class=\"data row184 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row184_col4\" class=\"data row184 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row184_col5\" class=\"data row184 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row184_col6\" class=\"data row184 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row184_col7\" class=\"data row184 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row184_col8\" class=\"data row184 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row185_col0\" class=\"data row185 col0\" >validmind.unit_metrics.regression.GiniCoefficient</td>\n",
+              "      <td id=\"T_0502a_row185_col1\" class=\"data row185 col1\" >Gini Coefficient</td>\n",
+              "      <td id=\"T_0502a_row185_col2\" class=\"data row185 col2\" >Calculates the Gini coefficient for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row185_col3\" class=\"data row185 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row185_col4\" class=\"data row185 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row185_col5\" class=\"data row185 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row185_col6\" class=\"data row185 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row185_col7\" class=\"data row185 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row185_col8\" class=\"data row185 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row186_col0\" class=\"data row186 col0\" >validmind.unit_metrics.regression.HuberLoss</td>\n",
+              "      <td id=\"T_0502a_row186_col1\" class=\"data row186 col1\" >Huber Loss</td>\n",
+              "      <td id=\"T_0502a_row186_col2\" class=\"data row186 col2\" >Calculates the Huber loss for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row186_col3\" class=\"data row186 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row186_col4\" class=\"data row186 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row186_col5\" class=\"data row186 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row186_col6\" class=\"data row186 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row186_col7\" class=\"data row186 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row186_col8\" class=\"data row186 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row187_col0\" class=\"data row187 col0\" >validmind.unit_metrics.regression.KolmogorovSmirnovStatistic</td>\n",
+              "      <td id=\"T_0502a_row187_col1\" class=\"data row187 col1\" >Kolmogorov Smirnov Statistic</td>\n",
+              "      <td id=\"T_0502a_row187_col2\" class=\"data row187 col2\" >Calculates the Kolmogorov-Smirnov statistic for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row187_col3\" class=\"data row187 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row187_col4\" class=\"data row187 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row187_col5\" class=\"data row187 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_0502a_row187_col6\" class=\"data row187 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row187_col7\" class=\"data row187 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row187_col8\" class=\"data row187 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row188_col0\" class=\"data row188 col0\" >validmind.unit_metrics.regression.MeanAbsoluteError</td>\n",
+              "      <td id=\"T_0502a_row188_col1\" class=\"data row188 col1\" >Mean Absolute Error</td>\n",
+              "      <td id=\"T_0502a_row188_col2\" class=\"data row188 col2\" >Calculates the mean absolute error for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row188_col3\" class=\"data row188 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row188_col4\" class=\"data row188 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row188_col5\" class=\"data row188 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row188_col6\" class=\"data row188 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row188_col7\" class=\"data row188 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row188_col8\" class=\"data row188 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row189_col0\" class=\"data row189 col0\" >validmind.unit_metrics.regression.MeanAbsolutePercentageError</td>\n",
+              "      <td id=\"T_0502a_row189_col1\" class=\"data row189 col1\" >Mean Absolute Percentage Error</td>\n",
+              "      <td id=\"T_0502a_row189_col2\" class=\"data row189 col2\" >Calculates the mean absolute percentage error for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row189_col3\" class=\"data row189 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row189_col4\" class=\"data row189 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row189_col5\" class=\"data row189 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row189_col6\" class=\"data row189 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row189_col7\" class=\"data row189 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row189_col8\" class=\"data row189 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row190_col0\" class=\"data row190 col0\" >validmind.unit_metrics.regression.MeanBiasDeviation</td>\n",
+              "      <td id=\"T_0502a_row190_col1\" class=\"data row190 col1\" >Mean Bias Deviation</td>\n",
+              "      <td id=\"T_0502a_row190_col2\" class=\"data row190 col2\" >Calculates the mean bias deviation for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row190_col3\" class=\"data row190 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row190_col4\" class=\"data row190 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row190_col5\" class=\"data row190 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row190_col6\" class=\"data row190 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row190_col7\" class=\"data row190 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row190_col8\" class=\"data row190 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row191_col0\" class=\"data row191 col0\" >validmind.unit_metrics.regression.MeanSquaredError</td>\n",
+              "      <td id=\"T_0502a_row191_col1\" class=\"data row191 col1\" >Mean Squared Error</td>\n",
+              "      <td id=\"T_0502a_row191_col2\" class=\"data row191 col2\" >Calculates the mean squared error for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row191_col3\" class=\"data row191 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row191_col4\" class=\"data row191 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row191_col5\" class=\"data row191 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row191_col6\" class=\"data row191 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row191_col7\" class=\"data row191 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row191_col8\" class=\"data row191 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row192_col0\" class=\"data row192 col0\" >validmind.unit_metrics.regression.QuantileLoss</td>\n",
+              "      <td id=\"T_0502a_row192_col1\" class=\"data row192 col1\" >Quantile Loss</td>\n",
+              "      <td id=\"T_0502a_row192_col2\" class=\"data row192 col2\" >Calculates the quantile loss for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row192_col3\" class=\"data row192 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row192_col4\" class=\"data row192 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row192_col5\" class=\"data row192 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row192_col6\" class=\"data row192 col6\" >{'quantile': {'type': '_empty', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_0502a_row192_col7\" class=\"data row192 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row192_col8\" class=\"data row192 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row193_col0\" class=\"data row193 col0\" >validmind.unit_metrics.regression.RSquaredScore</td>\n",
+              "      <td id=\"T_0502a_row193_col1\" class=\"data row193 col1\" >R Squared Score</td>\n",
+              "      <td id=\"T_0502a_row193_col2\" class=\"data row193 col2\" >Calculates the R-squared score for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row193_col3\" class=\"data row193 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row193_col4\" class=\"data row193 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row193_col5\" class=\"data row193 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row193_col6\" class=\"data row193 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row193_col7\" class=\"data row193 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row193_col8\" class=\"data row193 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_0502a_row194_col0\" class=\"data row194 col0\" >validmind.unit_metrics.regression.RootMeanSquaredError</td>\n",
+              "      <td id=\"T_0502a_row194_col1\" class=\"data row194 col1\" >Root Mean Squared Error</td>\n",
+              "      <td id=\"T_0502a_row194_col2\" class=\"data row194 col2\" >Calculates the root mean squared error for a regression model.</td>\n",
+              "      <td id=\"T_0502a_row194_col3\" class=\"data row194 col3\" >False</td>\n",
+              "      <td id=\"T_0502a_row194_col4\" class=\"data row194 col4\" >False</td>\n",
+              "      <td id=\"T_0502a_row194_col5\" class=\"data row194 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_0502a_row194_col6\" class=\"data row194 col6\" >{}</td>\n",
+              "      <td id=\"T_0502a_row194_col7\" class=\"data row194 col7\" >['regression']</td>\n",
+              "      <td id=\"T_0502a_row194_col8\" class=\"data row194 col8\" >['regression']</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x37556ee20>"
+              "<pandas.io.formats.style.Styler at 0x38000a670>"
             ]
           },
           "execution_count": 2,
@@ -1912,20 +2304,20 @@
         {
           "data": {
             "text/plain": [
-              "['time_series_forecasting',\n",
-              " 'feature_extraction',\n",
-              " 'text_qa',\n",
-              " 'text_generation',\n",
-              " 'residual_analysis',\n",
-              " 'visualization',\n",
+              "['text_qa',\n",
+              " 'classification',\n",
+              " 'data_validation',\n",
               " 'text_classification',\n",
+              " 'feature_extraction',\n",
               " 'regression',\n",
-              " 'nlp',\n",
-              " 'text_summarization',\n",
-              " 'data_validation',\n",
-              " 'classification',\n",
+              " 'visualization',\n",
               " 'clustering',\n",
-              " 'monitoring']"
+              " 'time_series_forecasting',\n",
+              " 'text_summarization',\n",
+              " 'nlp',\n",
+              " 'residual_analysis',\n",
+              " 'monitoring',\n",
+              " 'text_generation']"
             ]
           },
           "execution_count": 3,
@@ -1945,66 +2337,66 @@
         {
           "data": {
             "text/plain": [
-              "['few_shot',\n",
-              " 'ragas',\n",
-              " 'bias_and_fairness',\n",
-              " 'AUC',\n",
-              " 'visualization',\n",
-              " 'rag_performance',\n",
-              " 'logistic_regression',\n",
-              " 'model_validation',\n",
-              " 'credit_risk',\n",
-              " 'model_selection',\n",
-              " 'linear_regression',\n",
+              "['senstivity_analysis',\n",
+              " 'calibration',\n",
               " 'clustering',\n",
-              " 'data_distribution',\n",
-              " 'model_explainability',\n",
-              " 'frequency_analysis',\n",
-              " 'model_interpretation',\n",
+              " 'anomaly_detection',\n",
+              " 'nlp',\n",
+              " 'classification_metrics',\n",
+              " 'dimensionality_reduction',\n",
+              " 'tabular_data',\n",
               " 'time_series_data',\n",
-              " 'forecasting',\n",
+              " 'model_predictions',\n",
+              " 'feature_selection',\n",
+              " 'correlation',\n",
+              " 'frequency_analysis',\n",
+              " 'embeddings',\n",
+              " 'regression',\n",
               " 'llm',\n",
+              " 'statsmodels',\n",
+              " 'ragas',\n",
+              " 'model_performance',\n",
+              " 'model_validation',\n",
+              " 'rag_performance',\n",
+              " 'model_training',\n",
+              " 'qualitative',\n",
+              " 'classification',\n",
+              " 'kmeans',\n",
               " 'multiclass_classification',\n",
-              " 'data_validation',\n",
+              " 'linear_regression',\n",
+              " 'data_quality',\n",
+              " 'text_data',\n",
               " 'binary_classification',\n",
+              " 'threshold_optimization',\n",
               " 'stationarity',\n",
-              " 'senstivity_analysis',\n",
-              " 'retrieval_performance',\n",
-              " 'categorical_data',\n",
-              " 'seasonality',\n",
-              " 'qualitative',\n",
+              " 'bias_and_fairness',\n",
+              " 'scorecard',\n",
+              " 'model_explainability',\n",
               " 'model_comparison',\n",
-              " 'model_training',\n",
-              " 'data_quality',\n",
-              " 'regression',\n",
-              " 'anomaly_detection',\n",
-              " 'calibration',\n",
-              " 'model_predictions',\n",
-              " 'dimensionality_reduction',\n",
-              " 'descriptive_statistics',\n",
-              " 'classification',\n",
-              " 'unit_root_test',\n",
-              " 'metadata',\n",
-              " 'threshold_optimization',\n",
-              " 'model_diagnosis',\n",
-              " 'feature_selection',\n",
-              " 'data_analysis',\n",
+              " 'numerical_data',\n",
+              " 'sklearn',\n",
+              " 'model_selection',\n",
+              " 'retrieval_performance',\n",
+              " 'zero_shot',\n",
               " 'statistical_test',\n",
-              " 'embeddings',\n",
+              " 'descriptive_statistics',\n",
+              " 'seasonality',\n",
               " 'analysis',\n",
+              " 'data_validation',\n",
+              " 'data_distribution',\n",
               " 'feature_importance',\n",
-              " 'scorecard',\n",
-              " 'correlation',\n",
-              " 'classification_metrics',\n",
-              " 'nlp',\n",
-              " 'sklearn',\n",
-              " 'kmeans',\n",
-              " 'statsmodels',\n",
-              " 'numerical_data',\n",
-              " 'zero_shot',\n",
-              " 'text_data',\n",
-              " 'tabular_data',\n",
-              " 'model_performance']"
+              " 'metadata',\n",
+              " 'few_shot',\n",
+              " 'visualization',\n",
+              " 'credit_risk',\n",
+              " 'forecasting',\n",
+              " 'AUC',\n",
+              " 'logistic_regression',\n",
+              " 'model_diagnosis',\n",
+              " 'model_interpretation',\n",
+              " 'unit_root_test',\n",
+              " 'categorical_data',\n",
+              " 'data_analysis']"
             ]
           },
           "execution_count": 4,
@@ -2032,82 +2424,82 @@
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             "text/html": [
               "<style type=\"text/css\">\n",
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-              "<table id=\"T_fae72\">\n",
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               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_fae72_level0_col0\" class=\"col_heading level0 col0\" >Task</th>\n",
-              "      <th id=\"T_fae72_level0_col1\" class=\"col_heading level0 col1\" >Tags</th>\n",
+              "      <th id=\"T_ac294_level0_col0\" class=\"col_heading level0 col0\" >Task</th>\n",
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-              "      <td id=\"T_fae72_row0_col0\" class=\"data row0 col0\" >regression</td>\n",
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+              "      <td id=\"T_ac294_row0_col0\" class=\"data row0 col0\" >regression</td>\n",
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               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row1_col0\" class=\"data row1 col0\" >classification</td>\n",
-              "      <td id=\"T_fae72_row1_col1\" class=\"data row1 col1\" >bias_and_fairness, AUC, visualization, logistic_regression, model_validation, credit_risk, linear_regression, data_distribution, time_series_data, multiclass_classification, binary_classification, categorical_data, model_comparison, model_training, data_quality, anomaly_detection, calibration, descriptive_statistics, classification, metadata, model_diagnosis, threshold_optimization, feature_selection, data_analysis, statistical_test, classification_metrics, feature_importance, scorecard, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_performance</td>\n",
+              "      <td id=\"T_ac294_row1_col0\" class=\"data row1 col0\" >classification</td>\n",
+              "      <td id=\"T_ac294_row1_col1\" class=\"data row1 col1\" >calibration, anomaly_detection, classification_metrics, tabular_data, time_series_data, feature_selection, correlation, statsmodels, model_performance, model_validation, model_training, classification, multiclass_classification, linear_regression, data_quality, text_data, binary_classification, threshold_optimization, bias_and_fairness, scorecard, model_comparison, numerical_data, sklearn, statistical_test, descriptive_statistics, feature_importance, data_distribution, metadata, visualization, credit_risk, AUC, logistic_regression, model_diagnosis, categorical_data, data_analysis</td>\n",
               "    </tr>\n",
               "    <tr>\n",
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-              "      <td id=\"T_fae72_row2_col1\" class=\"data row2 col1\" >few_shot, ragas, visualization, frequency_analysis, model_comparison, feature_importance, time_series_data, nlp, llm, sklearn, multiclass_classification, zero_shot, text_data, binary_classification, retrieval_performance, tabular_data, model_performance, model_diagnosis</td>\n",
+              "      <td id=\"T_ac294_row2_col0\" class=\"data row2 col0\" >text_classification</td>\n",
+              "      <td id=\"T_ac294_row2_col1\" class=\"data row2 col1\" >model_performance, feature_importance, multiclass_classification, few_shot, frequency_analysis, zero_shot, text_data, visualization, llm, binary_classification, ragas, model_diagnosis, model_comparison, sklearn, nlp, retrieval_performance, tabular_data, time_series_data</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row3_col0\" class=\"data row3 col0\" >text_summarization</td>\n",
-              "      <td id=\"T_fae72_row3_col1\" class=\"data row3 col1\" >few_shot, ragas, qualitative, visualization, frequency_analysis, embeddings, rag_performance, time_series_data, nlp, llm, zero_shot, text_data, dimensionality_reduction, retrieval_performance, tabular_data</td>\n",
+              "      <td id=\"T_ac294_row3_col0\" class=\"data row3 col0\" >text_summarization</td>\n",
+              "      <td id=\"T_ac294_row3_col1\" class=\"data row3 col1\" >qualitative, few_shot, frequency_analysis, embeddings, zero_shot, text_data, visualization, llm, rag_performance, ragas, retrieval_performance, nlp, dimensionality_reduction, tabular_data, time_series_data</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row4_col0\" class=\"data row4 col0\" >data_validation</td>\n",
-              "      <td id=\"T_fae72_row4_col1\" class=\"data row4 col1\" >stationarity, time_series_data, statsmodels, unit_root_test</td>\n",
+              "      <td id=\"T_ac294_row4_col0\" class=\"data row4 col0\" >data_validation</td>\n",
+              "      <td id=\"T_ac294_row4_col1\" class=\"data row4 col1\" >stationarity, statsmodels, unit_root_test, time_series_data</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row5_col0\" class=\"data row5 col0\" >time_series_forecasting</td>\n",
-              "      <td id=\"T_fae72_row5_col1\" class=\"data row5 col1\" >model_explainability, visualization, time_series_data, sklearn, model_predictions, data_validation, model_performance, model_training, metadata</td>\n",
+              "      <td id=\"T_ac294_row5_col0\" class=\"data row5 col0\" >time_series_forecasting</td>\n",
+              "      <td id=\"T_ac294_row5_col1\" class=\"data row5 col1\" >model_training, data_validation, metadata, visualization, model_explainability, sklearn, model_performance, model_predictions, time_series_data</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row6_col0\" class=\"data row6 col0\" >nlp</td>\n",
-              "      <td id=\"T_fae72_row6_col1\" class=\"data row6 col1\" >visualization, frequency_analysis, data_validation, nlp, text_data</td>\n",
+              "      <td id=\"T_ac294_row6_col0\" class=\"data row6 col0\" >nlp</td>\n",
+              "      <td id=\"T_ac294_row6_col1\" class=\"data row6 col1\" >data_validation, frequency_analysis, text_data, visualization, nlp</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row7_col0\" class=\"data row7 col0\" >clustering</td>\n",
-              "      <td id=\"T_fae72_row7_col1\" class=\"data row7 col1\" >sklearn, kmeans, clustering, model_performance</td>\n",
+              "      <td id=\"T_ac294_row7_col0\" class=\"data row7 col0\" >clustering</td>\n",
+              "      <td id=\"T_ac294_row7_col1\" class=\"data row7 col1\" >clustering, model_performance, kmeans, sklearn</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row8_col0\" class=\"data row8 col0\" >residual_analysis</td>\n",
-              "      <td id=\"T_fae72_row8_col1\" class=\"data row8 col1\" >regression</td>\n",
+              "      <td id=\"T_ac294_row8_col0\" class=\"data row8 col0\" >residual_analysis</td>\n",
+              "      <td id=\"T_ac294_row8_col1\" class=\"data row8 col1\" >regression</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row9_col0\" class=\"data row9 col0\" >visualization</td>\n",
-              "      <td id=\"T_fae72_row9_col1\" class=\"data row9 col1\" >regression</td>\n",
+              "      <td id=\"T_ac294_row9_col0\" class=\"data row9 col0\" >visualization</td>\n",
+              "      <td id=\"T_ac294_row9_col1\" class=\"data row9 col1\" >regression</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row10_col0\" class=\"data row10 col0\" >feature_extraction</td>\n",
-              "      <td id=\"T_fae72_row10_col1\" class=\"data row10 col1\" >text_data, llm, visualization, embeddings</td>\n",
+              "      <td id=\"T_ac294_row10_col0\" class=\"data row10 col0\" >feature_extraction</td>\n",
+              "      <td id=\"T_ac294_row10_col1\" class=\"data row10 col1\" >embeddings, text_data, visualization, llm</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row11_col0\" class=\"data row11 col0\" >text_qa</td>\n",
-              "      <td id=\"T_fae72_row11_col1\" class=\"data row11 col1\" >ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance</td>\n",
+              "      <td id=\"T_ac294_row11_col0\" class=\"data row11 col0\" >text_qa</td>\n",
+              "      <td id=\"T_ac294_row11_col1\" class=\"data row11 col1\" >qualitative, embeddings, visualization, llm, rag_performance, ragas, dimensionality_reduction, retrieval_performance</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row12_col0\" class=\"data row12 col0\" >text_generation</td>\n",
-              "      <td id=\"T_fae72_row12_col1\" class=\"data row12 col1\" >ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance</td>\n",
+              "      <td id=\"T_ac294_row12_col0\" class=\"data row12 col0\" >text_generation</td>\n",
+              "      <td id=\"T_ac294_row12_col1\" class=\"data row12 col1\" >qualitative, embeddings, visualization, llm, rag_performance, ragas, dimensionality_reduction, retrieval_performance</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_fae72_row13_col0\" class=\"data row13 col0\" >monitoring</td>\n",
-              "      <td id=\"T_fae72_row13_col1\" class=\"data row13 col1\" >visualization</td>\n",
+              "      <td id=\"T_ac294_row13_col0\" class=\"data row13 col0\" >monitoring</td>\n",
+              "      <td id=\"T_ac294_row13_col1\" class=\"data row13 col1\" >visualization</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x3618a93a0>"
+              "<pandas.io.formats.style.Styler at 0x38000adc0>"
             ]
           },
           "execution_count": 5,
@@ -2146,418 +2538,506 @@
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#T_05adb_row7_col0, #T_05adb_row7_col1, #T_05adb_row7_col2, #T_05adb_row7_col3, #T_05adb_row7_col4, #T_05adb_row7_col5, #T_05adb_row7_col6, #T_05adb_row8_col0, #T_05adb_row8_col1, #T_05adb_row8_col2, #T_05adb_row8_col3, #T_05adb_row8_col4, #T_05adb_row8_col5, #T_05adb_row8_col6, #T_05adb_row9_col0, #T_05adb_row9_col1, #T_05adb_row9_col2, #T_05adb_row9_col3, #T_05adb_row9_col4, #T_05adb_row9_col5, #T_05adb_row9_col6, #T_05adb_row10_col0, #T_05adb_row10_col1, #T_05adb_row10_col2, #T_05adb_row10_col3, #T_05adb_row10_col4, #T_05adb_row10_col5, #T_05adb_row10_col6, #T_05adb_row11_col0, #T_05adb_row11_col1, #T_05adb_row11_col2, #T_05adb_row11_col3, #T_05adb_row11_col4, #T_05adb_row11_col5, #T_05adb_row11_col6, #T_05adb_row12_col0, #T_05adb_row12_col1, #T_05adb_row12_col2, #T_05adb_row12_col3, #T_05adb_row12_col4, #T_05adb_row12_col5, #T_05adb_row12_col6, #T_05adb_row13_col0, #T_05adb_row13_col1, #T_05adb_row13_col2, #T_05adb_row13_col3, #T_05adb_row13_col4, #T_05adb_row13_col5, #T_05adb_row13_col6, #T_05adb_row14_col0, #T_05adb_row14_col1, #T_05adb_row14_col2, #T_05adb_row14_col3, #T_05adb_row14_col4, #T_05adb_row14_col5, #T_05adb_row14_col6, #T_05adb_row15_col0, #T_05adb_row15_col1, #T_05adb_row15_col2, #T_05adb_row15_col3, #T_05adb_row15_col4, #T_05adb_row15_col5, #T_05adb_row15_col6, #T_05adb_row16_col0, #T_05adb_row16_col1, #T_05adb_row16_col2, #T_05adb_row16_col3, #T_05adb_row16_col4, #T_05adb_row16_col5, #T_05adb_row16_col6, #T_05adb_row17_col0, #T_05adb_row17_col1, #T_05adb_row17_col2, #T_05adb_row17_col3, #T_05adb_row17_col4, #T_05adb_row17_col5, #T_05adb_row17_col6, #T_05adb_row18_col0, #T_05adb_row18_col1, #T_05adb_row18_col2, #T_05adb_row18_col3, #T_05adb_row18_col4, #T_05adb_row18_col5, #T_05adb_row18_col6, #T_05adb_row19_col0, #T_05adb_row19_col1, #T_05adb_row19_col2, #T_05adb_row19_col3, #T_05adb_row19_col4, #T_05adb_row19_col5, #T_05adb_row19_col6, #T_05adb_row20_col0, #T_05adb_row20_col1, #T_05adb_row20_col2, #T_05adb_row20_col3, #T_05adb_row20_col4, #T_05adb_row20_col5, #T_05adb_row20_col6, #T_05adb_row21_col0, #T_05adb_row21_col1, #T_05adb_row21_col2, #T_05adb_row21_col3, #T_05adb_row21_col4, #T_05adb_row21_col5, #T_05adb_row21_col6, #T_05adb_row22_col0, #T_05adb_row22_col1, #T_05adb_row22_col2, #T_05adb_row22_col3, #T_05adb_row22_col4, #T_05adb_row22_col5, #T_05adb_row22_col6, #T_05adb_row23_col0, #T_05adb_row23_col1, #T_05adb_row23_col2, #T_05adb_row23_col3, #T_05adb_row23_col4, #T_05adb_row23_col5, #T_05adb_row23_col6, #T_05adb_row24_col0, #T_05adb_row24_col1, #T_05adb_row24_col2, #T_05adb_row24_col3, #T_05adb_row24_col4, #T_05adb_row24_col5, #T_05adb_row24_col6, #T_05adb_row25_col0, #T_05adb_row25_col1, #T_05adb_row25_col2, #T_05adb_row25_col3, #T_05adb_row25_col4, #T_05adb_row25_col5, #T_05adb_row25_col6, #T_05adb_row26_col0, #T_05adb_row26_col1, #T_05adb_row26_col2, #T_05adb_row26_col3, #T_05adb_row26_col4, #T_05adb_row26_col5, #T_05adb_row26_col6, #T_05adb_row27_col0, #T_05adb_row27_col1, #T_05adb_row27_col2, #T_05adb_row27_col3, #T_05adb_row27_col4, #T_05adb_row27_col5, #T_05adb_row27_col6, #T_05adb_row28_col0, #T_05adb_row28_col1, #T_05adb_row28_col2, #T_05adb_row28_col3, #T_05adb_row28_col4, #T_05adb_row28_col5, #T_05adb_row28_col6, #T_05adb_row29_col0, #T_05adb_row29_col1, #T_05adb_row29_col2, #T_05adb_row29_col3, #T_05adb_row29_col4, #T_05adb_row29_col5, #T_05adb_row29_col6, #T_05adb_row30_col0, #T_05adb_row30_col1, #T_05adb_row30_col2, #T_05adb_row30_col3, #T_05adb_row30_col4, #T_05adb_row30_col5, #T_05adb_row30_col6, #T_05adb_row31_col0, #T_05adb_row31_col1, #T_05adb_row31_col2, #T_05adb_row31_col3, #T_05adb_row31_col4, #T_05adb_row31_col5, #T_05adb_row31_col6, #T_05adb_row32_col0, #T_05adb_row32_col1, #T_05adb_row32_col2, #T_05adb_row32_col3, #T_05adb_row32_col4, #T_05adb_row32_col5, #T_05adb_row32_col6, #T_05adb_row33_col0, #T_05adb_row33_col1, #T_05adb_row33_col2, #T_05adb_row33_col3, #T_05adb_row33_col4, #T_05adb_row33_col5, #T_05adb_row33_col6, #T_05adb_row34_col0, #T_05adb_row34_col1, #T_05adb_row34_col2, #T_05adb_row34_col3, #T_05adb_row34_col4, #T_05adb_row34_col5, #T_05adb_row34_col6, #T_05adb_row35_col0, #T_05adb_row35_col1, #T_05adb_row35_col2, #T_05adb_row35_col3, #T_05adb_row35_col4, #T_05adb_row35_col5, #T_05adb_row35_col6, #T_05adb_row36_col0, #T_05adb_row36_col1, #T_05adb_row36_col2, #T_05adb_row36_col3, #T_05adb_row36_col4, #T_05adb_row36_col5, #T_05adb_row36_col6, #T_05adb_row37_col0, #T_05adb_row37_col1, #T_05adb_row37_col2, #T_05adb_row37_col3, #T_05adb_row37_col4, #T_05adb_row37_col5, #T_05adb_row37_col6, #T_05adb_row38_col0, #T_05adb_row38_col1, #T_05adb_row38_col2, #T_05adb_row38_col3, #T_05adb_row38_col4, #T_05adb_row38_col5, #T_05adb_row38_col6, #T_05adb_row39_col0, #T_05adb_row39_col1, #T_05adb_row39_col2, #T_05adb_row39_col3, #T_05adb_row39_col4, #T_05adb_row39_col5, #T_05adb_row39_col6, #T_05adb_row40_col0, #T_05adb_row40_col1, #T_05adb_row40_col2, #T_05adb_row40_col3, #T_05adb_row40_col4, #T_05adb_row40_col5, #T_05adb_row40_col6, #T_05adb_row41_col0, #T_05adb_row41_col1, #T_05adb_row41_col2, #T_05adb_row41_col3, #T_05adb_row41_col4, #T_05adb_row41_col5, #T_05adb_row41_col6, #T_05adb_row42_col0, #T_05adb_row42_col1, #T_05adb_row42_col2, #T_05adb_row42_col3, #T_05adb_row42_col4, #T_05adb_row42_col5, #T_05adb_row42_col6 {\n",
+              "#T_326c3_row0_col0, #T_326c3_row0_col1, #T_326c3_row0_col2, #T_326c3_row0_col3, #T_326c3_row0_col4, #T_326c3_row0_col5, #T_326c3_row0_col6, #T_326c3_row0_col7, #T_326c3_row0_col8, #T_326c3_row1_col0, #T_326c3_row1_col1, #T_326c3_row1_col2, #T_326c3_row1_col3, #T_326c3_row1_col4, #T_326c3_row1_col5, #T_326c3_row1_col6, #T_326c3_row1_col7, #T_326c3_row1_col8, #T_326c3_row2_col0, #T_326c3_row2_col1, #T_326c3_row2_col2, #T_326c3_row2_col3, #T_326c3_row2_col4, #T_326c3_row2_col5, #T_326c3_row2_col6, #T_326c3_row2_col7, #T_326c3_row2_col8, #T_326c3_row3_col0, #T_326c3_row3_col1, #T_326c3_row3_col2, #T_326c3_row3_col3, #T_326c3_row3_col4, #T_326c3_row3_col5, #T_326c3_row3_col6, #T_326c3_row3_col7, #T_326c3_row3_col8, #T_326c3_row4_col0, #T_326c3_row4_col1, #T_326c3_row4_col2, #T_326c3_row4_col3, #T_326c3_row4_col4, #T_326c3_row4_col5, #T_326c3_row4_col6, #T_326c3_row4_col7, #T_326c3_row4_col8, #T_326c3_row5_col0, #T_326c3_row5_col1, #T_326c3_row5_col2, #T_326c3_row5_col3, #T_326c3_row5_col4, #T_326c3_row5_col5, #T_326c3_row5_col6, #T_326c3_row5_col7, #T_326c3_row5_col8, #T_326c3_row6_col0, #T_326c3_row6_col1, #T_326c3_row6_col2, #T_326c3_row6_col3, #T_326c3_row6_col4, #T_326c3_row6_col5, #T_326c3_row6_col6, #T_326c3_row6_col7, #T_326c3_row6_col8, #T_326c3_row7_col0, #T_326c3_row7_col1, #T_326c3_row7_col2, #T_326c3_row7_col3, #T_326c3_row7_col4, #T_326c3_row7_col5, #T_326c3_row7_col6, #T_326c3_row7_col7, #T_326c3_row7_col8, #T_326c3_row8_col0, #T_326c3_row8_col1, #T_326c3_row8_col2, #T_326c3_row8_col3, #T_326c3_row8_col4, #T_326c3_row8_col5, #T_326c3_row8_col6, #T_326c3_row8_col7, #T_326c3_row8_col8, #T_326c3_row9_col0, #T_326c3_row9_col1, #T_326c3_row9_col2, #T_326c3_row9_col3, #T_326c3_row9_col4, #T_326c3_row9_col5, #T_326c3_row9_col6, #T_326c3_row9_col7, #T_326c3_row9_col8, #T_326c3_row10_col0, #T_326c3_row10_col1, #T_326c3_row10_col2, #T_326c3_row10_col3, #T_326c3_row10_col4, #T_326c3_row10_col5, #T_326c3_row10_col6, #T_326c3_row10_col7, #T_326c3_row10_col8, #T_326c3_row11_col0, #T_326c3_row11_col1, #T_326c3_row11_col2, #T_326c3_row11_col3, #T_326c3_row11_col4, #T_326c3_row11_col5, #T_326c3_row11_col6, #T_326c3_row11_col7, #T_326c3_row11_col8, #T_326c3_row12_col0, #T_326c3_row12_col1, #T_326c3_row12_col2, #T_326c3_row12_col3, #T_326c3_row12_col4, #T_326c3_row12_col5, #T_326c3_row12_col6, #T_326c3_row12_col7, #T_326c3_row12_col8, #T_326c3_row13_col0, #T_326c3_row13_col1, #T_326c3_row13_col2, #T_326c3_row13_col3, #T_326c3_row13_col4, #T_326c3_row13_col5, #T_326c3_row13_col6, #T_326c3_row13_col7, #T_326c3_row13_col8, #T_326c3_row14_col0, #T_326c3_row14_col1, #T_326c3_row14_col2, #T_326c3_row14_col3, #T_326c3_row14_col4, #T_326c3_row14_col5, #T_326c3_row14_col6, #T_326c3_row14_col7, #T_326c3_row14_col8, #T_326c3_row15_col0, #T_326c3_row15_col1, #T_326c3_row15_col2, #T_326c3_row15_col3, #T_326c3_row15_col4, #T_326c3_row15_col5, #T_326c3_row15_col6, #T_326c3_row15_col7, #T_326c3_row15_col8, #T_326c3_row16_col0, #T_326c3_row16_col1, #T_326c3_row16_col2, #T_326c3_row16_col3, #T_326c3_row16_col4, #T_326c3_row16_col5, #T_326c3_row16_col6, #T_326c3_row16_col7, #T_326c3_row16_col8, #T_326c3_row17_col0, #T_326c3_row17_col1, #T_326c3_row17_col2, #T_326c3_row17_col3, #T_326c3_row17_col4, #T_326c3_row17_col5, #T_326c3_row17_col6, #T_326c3_row17_col7, #T_326c3_row17_col8, #T_326c3_row18_col0, #T_326c3_row18_col1, #T_326c3_row18_col2, #T_326c3_row18_col3, #T_326c3_row18_col4, #T_326c3_row18_col5, #T_326c3_row18_col6, #T_326c3_row18_col7, #T_326c3_row18_col8, #T_326c3_row19_col0, #T_326c3_row19_col1, #T_326c3_row19_col2, #T_326c3_row19_col3, #T_326c3_row19_col4, #T_326c3_row19_col5, #T_326c3_row19_col6, #T_326c3_row19_col7, #T_326c3_row19_col8, #T_326c3_row20_col0, #T_326c3_row20_col1, #T_326c3_row20_col2, #T_326c3_row20_col3, #T_326c3_row20_col4, #T_326c3_row20_col5, #T_326c3_row20_col6, #T_326c3_row20_col7, #T_326c3_row20_col8, #T_326c3_row21_col0, #T_326c3_row21_col1, #T_326c3_row21_col2, #T_326c3_row21_col3, #T_326c3_row21_col4, #T_326c3_row21_col5, #T_326c3_row21_col6, #T_326c3_row21_col7, #T_326c3_row21_col8, #T_326c3_row22_col0, #T_326c3_row22_col1, #T_326c3_row22_col2, #T_326c3_row22_col3, #T_326c3_row22_col4, #T_326c3_row22_col5, #T_326c3_row22_col6, #T_326c3_row22_col7, #T_326c3_row22_col8, #T_326c3_row23_col0, #T_326c3_row23_col1, #T_326c3_row23_col2, #T_326c3_row23_col3, #T_326c3_row23_col4, #T_326c3_row23_col5, #T_326c3_row23_col6, #T_326c3_row23_col7, #T_326c3_row23_col8, #T_326c3_row24_col0, #T_326c3_row24_col1, #T_326c3_row24_col2, #T_326c3_row24_col3, #T_326c3_row24_col4, #T_326c3_row24_col5, #T_326c3_row24_col6, #T_326c3_row24_col7, #T_326c3_row24_col8, #T_326c3_row25_col0, #T_326c3_row25_col1, #T_326c3_row25_col2, #T_326c3_row25_col3, #T_326c3_row25_col4, #T_326c3_row25_col5, #T_326c3_row25_col6, #T_326c3_row25_col7, #T_326c3_row25_col8, #T_326c3_row26_col0, #T_326c3_row26_col1, #T_326c3_row26_col2, #T_326c3_row26_col3, #T_326c3_row26_col4, #T_326c3_row26_col5, #T_326c3_row26_col6, #T_326c3_row26_col7, #T_326c3_row26_col8, #T_326c3_row27_col0, #T_326c3_row27_col1, #T_326c3_row27_col2, #T_326c3_row27_col3, #T_326c3_row27_col4, #T_326c3_row27_col5, #T_326c3_row27_col6, #T_326c3_row27_col7, #T_326c3_row27_col8, #T_326c3_row28_col0, #T_326c3_row28_col1, #T_326c3_row28_col2, #T_326c3_row28_col3, #T_326c3_row28_col4, #T_326c3_row28_col5, #T_326c3_row28_col6, #T_326c3_row28_col7, #T_326c3_row28_col8, #T_326c3_row29_col0, #T_326c3_row29_col1, #T_326c3_row29_col2, #T_326c3_row29_col3, #T_326c3_row29_col4, #T_326c3_row29_col5, #T_326c3_row29_col6, #T_326c3_row29_col7, #T_326c3_row29_col8, #T_326c3_row30_col0, #T_326c3_row30_col1, #T_326c3_row30_col2, #T_326c3_row30_col3, #T_326c3_row30_col4, #T_326c3_row30_col5, #T_326c3_row30_col6, #T_326c3_row30_col7, #T_326c3_row30_col8, #T_326c3_row31_col0, #T_326c3_row31_col1, #T_326c3_row31_col2, #T_326c3_row31_col3, #T_326c3_row31_col4, #T_326c3_row31_col5, #T_326c3_row31_col6, #T_326c3_row31_col7, #T_326c3_row31_col8, #T_326c3_row32_col0, #T_326c3_row32_col1, #T_326c3_row32_col2, #T_326c3_row32_col3, #T_326c3_row32_col4, #T_326c3_row32_col5, #T_326c3_row32_col6, #T_326c3_row32_col7, #T_326c3_row32_col8, #T_326c3_row33_col0, #T_326c3_row33_col1, #T_326c3_row33_col2, #T_326c3_row33_col3, #T_326c3_row33_col4, #T_326c3_row33_col5, #T_326c3_row33_col6, #T_326c3_row33_col7, #T_326c3_row33_col8, #T_326c3_row34_col0, #T_326c3_row34_col1, #T_326c3_row34_col2, #T_326c3_row34_col3, #T_326c3_row34_col4, #T_326c3_row34_col5, #T_326c3_row34_col6, #T_326c3_row34_col7, #T_326c3_row34_col8, #T_326c3_row35_col0, #T_326c3_row35_col1, #T_326c3_row35_col2, #T_326c3_row35_col3, #T_326c3_row35_col4, #T_326c3_row35_col5, #T_326c3_row35_col6, #T_326c3_row35_col7, #T_326c3_row35_col8, #T_326c3_row36_col0, #T_326c3_row36_col1, #T_326c3_row36_col2, #T_326c3_row36_col3, #T_326c3_row36_col4, #T_326c3_row36_col5, #T_326c3_row36_col6, #T_326c3_row36_col7, #T_326c3_row36_col8, 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#T_326c3_row42_col2, #T_326c3_row42_col3, #T_326c3_row42_col4, #T_326c3_row42_col5, #T_326c3_row42_col6, #T_326c3_row42_col7, #T_326c3_row42_col8 {\n",
               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_05adb\">\n",
+              "<table id=\"T_326c3\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_05adb_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_05adb_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_05adb_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_05adb_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_05adb_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
-              "      <th id=\"T_05adb_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
-              "      <th id=\"T_05adb_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
+              "      <th id=\"T_326c3_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
+              "      <th id=\"T_326c3_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
+              "      <th id=\"T_326c3_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+              "      <th id=\"T_326c3_level0_col3\" class=\"col_heading level0 col3\" >Has Figure</th>\n",
+              "      <th id=\"T_326c3_level0_col4\" class=\"col_heading level0 col4\" >Has Table</th>\n",
+              "      <th id=\"T_326c3_level0_col5\" class=\"col_heading level0 col5\" >Required Inputs</th>\n",
+              "      <th id=\"T_326c3_level0_col6\" class=\"col_heading level0 col6\" >Params</th>\n",
+              "      <th id=\"T_326c3_level0_col7\" class=\"col_heading level0 col7\" >Tags</th>\n",
+              "      <th id=\"T_326c3_level0_col8\" class=\"col_heading level0 col8\" >Tasks</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_05adb_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
-              "      <td id=\"T_05adb_row0_col1\" class=\"data row0 col1\" >Cluster Size Distribution</td>\n",
-              "      <td id=\"T_05adb_row0_col2\" class=\"data row0 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
-              "      <td id=\"T_05adb_row0_col3\" class=\"data row0 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row0_col5\" class=\"data row0 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row0_col6\" class=\"data row0 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
-              "      <td id=\"T_05adb_row1_col1\" class=\"data row1 col1\" >Time Series R2 Square By Segments</td>\n",
-              "      <td id=\"T_05adb_row1_col2\" class=\"data row1 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
-              "      <td id=\"T_05adb_row1_col3\" class=\"data row1 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row1_col4\" class=\"data row1 col4\" >{'segments': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row1_col5\" class=\"data row1 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_05adb_row1_col6\" class=\"data row1 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
-              "      <td id=\"T_05adb_row2_col1\" class=\"data row2 col1\" >Adjusted Mutual Information</td>\n",
-              "      <td id=\"T_05adb_row2_col2\" class=\"data row2 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
-              "      <td id=\"T_05adb_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row2_col5\" class=\"data row2 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_05adb_row2_col6\" class=\"data row2 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
-              "      <td id=\"T_05adb_row3_col1\" class=\"data row3 col1\" >Adjusted Rand Index</td>\n",
-              "      <td id=\"T_05adb_row3_col2\" class=\"data row3 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
-              "      <td id=\"T_05adb_row3_col3\" class=\"data row3 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row3_col5\" class=\"data row3 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_05adb_row3_col6\" class=\"data row3 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
-              "      <td id=\"T_05adb_row4_col1\" class=\"data row4 col1\" >Calibration Curve</td>\n",
-              "      <td id=\"T_05adb_row4_col2\" class=\"data row4 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
-              "      <td id=\"T_05adb_row4_col3\" class=\"data row4 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row4_col4\" class=\"data row4 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_05adb_row4_col5\" class=\"data row4 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
-              "      <td id=\"T_05adb_row4_col6\" class=\"data row4 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row5_col0\" class=\"data row5 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_05adb_row5_col1\" class=\"data row5 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_05adb_row5_col2\" class=\"data row5 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_05adb_row5_col3\" class=\"data row5 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row5_col4\" class=\"data row5 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
-              "      <td id=\"T_05adb_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row6_col0\" class=\"data row6 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
-              "      <td id=\"T_05adb_row6_col1\" class=\"data row6 col1\" >Classifier Threshold Optimization</td>\n",
-              "      <td id=\"T_05adb_row6_col2\" class=\"data row6 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
-              "      <td id=\"T_05adb_row6_col3\" class=\"data row6 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row6_col4\" class=\"data row6 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row6_col5\" class=\"data row6 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
-              "      <td id=\"T_05adb_row6_col6\" class=\"data row6 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
-              "      <td id=\"T_05adb_row7_col1\" class=\"data row7 col1\" >Cluster Cosine Similarity</td>\n",
-              "      <td id=\"T_05adb_row7_col2\" class=\"data row7 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
-              "      <td id=\"T_05adb_row7_col3\" class=\"data row7 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row7_col4\" class=\"data row7 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row7_col5\" class=\"data row7 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_05adb_row7_col6\" class=\"data row7 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
-              "      <td id=\"T_05adb_row8_col1\" class=\"data row8 col1\" >Cluster Performance Metrics</td>\n",
-              "      <td id=\"T_05adb_row8_col2\" class=\"data row8 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
-              "      <td id=\"T_05adb_row8_col3\" class=\"data row8 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row8_col4\" class=\"data row8 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row8_col5\" class=\"data row8 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_05adb_row8_col6\" class=\"data row8 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
-              "      <td id=\"T_05adb_row9_col1\" class=\"data row9 col1\" >Completeness Score</td>\n",
-              "      <td id=\"T_05adb_row9_col2\" class=\"data row9 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
-              "      <td id=\"T_05adb_row9_col3\" class=\"data row9 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row9_col5\" class=\"data row9 col5\" >['sklearn', 'model_performance', 'clustering']</td>\n",
-              "      <td id=\"T_05adb_row9_col6\" class=\"data row9 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_05adb_row10_col1\" class=\"data row10 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_05adb_row10_col2\" class=\"data row10 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_05adb_row10_col3\" class=\"data row10 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row10_col4\" class=\"data row10 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_05adb_row10_col5\" class=\"data row10 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row10_col6\" class=\"data row10 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
-              "      <td id=\"T_05adb_row11_col1\" class=\"data row11 col1\" >Feature Importance</td>\n",
-              "      <td id=\"T_05adb_row11_col2\" class=\"data row11 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
-              "      <td id=\"T_05adb_row11_col3\" class=\"data row11 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row11_col4\" class=\"data row11 col4\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
-              "      <td id=\"T_05adb_row11_col5\" class=\"data row11 col5\" >['model_explainability', 'sklearn']</td>\n",
-              "      <td id=\"T_05adb_row11_col6\" class=\"data row11 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
-              "      <td id=\"T_05adb_row12_col1\" class=\"data row12 col1\" >Fowlkes Mallows Score</td>\n",
-              "      <td id=\"T_05adb_row12_col2\" class=\"data row12 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
-              "      <td id=\"T_05adb_row12_col3\" class=\"data row12 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row12_col4\" class=\"data row12 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row12_col5\" class=\"data row12 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row12_col6\" class=\"data row12 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
-              "      <td id=\"T_05adb_row13_col1\" class=\"data row13 col1\" >Homogeneity Score</td>\n",
-              "      <td id=\"T_05adb_row13_col2\" class=\"data row13 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
-              "      <td id=\"T_05adb_row13_col3\" class=\"data row13 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row13_col4\" class=\"data row13 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row13_col5\" class=\"data row13 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row13_col6\" class=\"data row13 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_05adb_row14_col1\" class=\"data row14 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_05adb_row14_col2\" class=\"data row14 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
-              "      <td id=\"T_05adb_row14_col3\" class=\"data row14 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row14_col4\" class=\"data row14 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row14_col5\" class=\"data row14 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row14_col6\" class=\"data row14 col6\" >['clustering', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
-              "      <td id=\"T_05adb_row15_col1\" class=\"data row15 col1\" >K Means Clusters Optimization</td>\n",
-              "      <td id=\"T_05adb_row15_col2\" class=\"data row15 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
-              "      <td id=\"T_05adb_row15_col3\" class=\"data row15 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row15_col4\" class=\"data row15 col4\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row15_col5\" class=\"data row15 col5\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
-              "      <td id=\"T_05adb_row15_col6\" class=\"data row15 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_05adb_row16_col1\" class=\"data row16 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_05adb_row16_col2\" class=\"data row16 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_05adb_row16_col3\" class=\"data row16 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row16_col4\" class=\"data row16 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_05adb_row16_col5\" class=\"data row16 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row16_col6\" class=\"data row16 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_05adb_row17_col1\" class=\"data row17 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_05adb_row17_col2\" class=\"data row17 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
-              "      <td id=\"T_05adb_row17_col3\" class=\"data row17 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row17_col4\" class=\"data row17 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_05adb_row17_col5\" class=\"data row17 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row17_col6\" class=\"data row17 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_05adb_row18_col1\" class=\"data row18 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_05adb_row18_col2\" class=\"data row18 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_05adb_row18_col3\" class=\"data row18 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row18_col4\" class=\"data row18 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_05adb_row18_col5\" class=\"data row18 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row18_col6\" class=\"data row18 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
-              "      <td id=\"T_05adb_row19_col1\" class=\"data row19 col1\" >Model Parameters</td>\n",
-              "      <td id=\"T_05adb_row19_col2\" class=\"data row19 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
-              "      <td id=\"T_05adb_row19_col3\" class=\"data row19 col3\" >['model']</td>\n",
-              "      <td id=\"T_05adb_row19_col4\" class=\"data row19 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row19_col5\" class=\"data row19 col5\" >['model_training', 'metadata']</td>\n",
-              "      <td id=\"T_05adb_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_05adb_row20_col1\" class=\"data row20 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_05adb_row20_col2\" class=\"data row20 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_05adb_row20_col3\" class=\"data row20 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_05adb_row20_col4\" class=\"data row20 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row20_col5\" class=\"data row20 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
-              "      <td id=\"T_05adb_row20_col6\" class=\"data row20 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_05adb_row21_col1\" class=\"data row21 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_05adb_row21_col2\" class=\"data row21 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
-              "      <td id=\"T_05adb_row21_col3\" class=\"data row21 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_05adb_row21_col4\" class=\"data row21 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
-              "      <td id=\"T_05adb_row21_col5\" class=\"data row21 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
-              "      <td id=\"T_05adb_row21_col6\" class=\"data row21 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_05adb_row22_col1\" class=\"data row22 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_05adb_row22_col2\" class=\"data row22 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_05adb_row22_col3\" class=\"data row22 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row22_col4\" class=\"data row22 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row22_col5\" class=\"data row22 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row22_col6\" class=\"data row22 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_05adb_row23_col1\" class=\"data row23 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_05adb_row23_col2\" class=\"data row23 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_05adb_row23_col3\" class=\"data row23 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row23_col4\" class=\"data row23 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
-              "      <td id=\"T_05adb_row23_col5\" class=\"data row23 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row23_col6\" class=\"data row23 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_05adb_row24_col1\" class=\"data row24 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_05adb_row24_col2\" class=\"data row24 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_05adb_row24_col3\" class=\"data row24 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row24_col4\" class=\"data row24 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row24_col5\" class=\"data row24 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row24_col6\" class=\"data row24 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_05adb_row25_col1\" class=\"data row25 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_05adb_row25_col2\" class=\"data row25 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_05adb_row25_col3\" class=\"data row25 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row25_col4\" class=\"data row25 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row25_col5\" class=\"data row25 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row25_col6\" class=\"data row25 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
-              "      <td id=\"T_05adb_row26_col1\" class=\"data row26 col1\" >Regression Errors</td>\n",
-              "      <td id=\"T_05adb_row26_col2\" class=\"data row26 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
-              "      <td id=\"T_05adb_row26_col3\" class=\"data row26 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row26_col4\" class=\"data row26 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row26_col5\" class=\"data row26 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row26_col6\" class=\"data row26 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
-              "      <td id=\"T_05adb_row27_col1\" class=\"data row27 col1\" >Regression Errors Comparison</td>\n",
-              "      <td id=\"T_05adb_row27_col2\" class=\"data row27 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
-              "      <td id=\"T_05adb_row27_col3\" class=\"data row27 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_05adb_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row27_col5\" class=\"data row27 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_05adb_row27_col6\" class=\"data row27 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
-              "      <td id=\"T_05adb_row28_col1\" class=\"data row28 col1\" >Regression Performance</td>\n",
-              "      <td id=\"T_05adb_row28_col2\" class=\"data row28 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
-              "      <td id=\"T_05adb_row28_col3\" class=\"data row28 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row28_col5\" class=\"data row28 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row28_col6\" class=\"data row28 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
-              "      <td id=\"T_05adb_row29_col1\" class=\"data row29 col1\" >Regression R2 Square</td>\n",
-              "      <td id=\"T_05adb_row29_col2\" class=\"data row29 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
-              "      <td id=\"T_05adb_row29_col3\" class=\"data row29 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row29_col4\" class=\"data row29 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row29_col5\" class=\"data row29 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row29_col6\" class=\"data row29 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
-              "      <td id=\"T_05adb_row30_col1\" class=\"data row30 col1\" >Regression R2 Square Comparison</td>\n",
-              "      <td id=\"T_05adb_row30_col2\" class=\"data row30 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
-              "      <td id=\"T_05adb_row30_col3\" class=\"data row30 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_05adb_row30_col4\" class=\"data row30 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row30_col5\" class=\"data row30 col5\" >['model_performance', 'sklearn']</td>\n",
-              "      <td id=\"T_05adb_row30_col6\" class=\"data row30 col6\" >['regression', 'time_series_forecasting']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_05adb_row31_col1\" class=\"data row31 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_05adb_row31_col2\" class=\"data row31 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
-              "      <td id=\"T_05adb_row31_col3\" class=\"data row31 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row31_col4\" class=\"data row31 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_05adb_row31_col5\" class=\"data row31 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row31_col6\" class=\"data row31 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_05adb_row32_col1\" class=\"data row32 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_05adb_row32_col2\" class=\"data row32 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_05adb_row32_col3\" class=\"data row32 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row32_col4\" class=\"data row32 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row32_col5\" class=\"data row32 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row32_col6\" class=\"data row32 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
-              "      <td id=\"T_05adb_row33_col1\" class=\"data row33 col1\" >Score Probability Alignment</td>\n",
-              "      <td id=\"T_05adb_row33_col2\" class=\"data row33 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
-              "      <td id=\"T_05adb_row33_col3\" class=\"data row33 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row33_col4\" class=\"data row33 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_05adb_row33_col5\" class=\"data row33 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
-              "      <td id=\"T_05adb_row33_col6\" class=\"data row33 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
-              "      <td id=\"T_05adb_row34_col1\" class=\"data row34 col1\" >Silhouette Plot</td>\n",
-              "      <td id=\"T_05adb_row34_col2\" class=\"data row34 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
-              "      <td id=\"T_05adb_row34_col3\" class=\"data row34 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_05adb_row34_col4\" class=\"data row34 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row34_col5\" class=\"data row34 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row34_col6\" class=\"data row34 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_05adb_row35_col1\" class=\"data row35 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_05adb_row35_col2\" class=\"data row35 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_05adb_row35_col3\" class=\"data row35 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row35_col4\" class=\"data row35 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_05adb_row35_col5\" class=\"data row35 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row35_col6\" class=\"data row35 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
-              "      <td id=\"T_05adb_row36_col1\" class=\"data row36 col1\" >V Measure</td>\n",
-              "      <td id=\"T_05adb_row36_col2\" class=\"data row36 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
-              "      <td id=\"T_05adb_row36_col3\" class=\"data row36 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_05adb_row36_col4\" class=\"data row36 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row36_col5\" class=\"data row36 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row36_col6\" class=\"data row36 col6\" >['clustering']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_05adb_row37_col1\" class=\"data row37 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_05adb_row37_col2\" class=\"data row37 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_05adb_row37_col3\" class=\"data row37 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row37_col4\" class=\"data row37 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_05adb_row37_col5\" class=\"data row37 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row37_col6\" class=\"data row37 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row38_col0\" class=\"data row38 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
-              "      <td id=\"T_05adb_row38_col1\" class=\"data row38 col1\" >Calibration Curve Drift</td>\n",
-              "      <td id=\"T_05adb_row38_col2\" class=\"data row38 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_05adb_row38_col3\" class=\"data row38 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row38_col4\" class=\"data row38 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
-              "      <td id=\"T_05adb_row38_col5\" class=\"data row38 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row38_col6\" class=\"data row38 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row39_col0\" class=\"data row39 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
-              "      <td id=\"T_05adb_row39_col1\" class=\"data row39 col1\" >Class Discrimination Drift</td>\n",
-              "      <td id=\"T_05adb_row39_col2\" class=\"data row39 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_05adb_row39_col3\" class=\"data row39 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row39_col4\" class=\"data row39 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_05adb_row39_col5\" class=\"data row39 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row39_col6\" class=\"data row39 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row40_col0\" class=\"data row40 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
-              "      <td id=\"T_05adb_row40_col1\" class=\"data row40 col1\" >Classification Accuracy Drift</td>\n",
-              "      <td id=\"T_05adb_row40_col2\" class=\"data row40 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_05adb_row40_col3\" class=\"data row40 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row40_col4\" class=\"data row40 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_05adb_row40_col5\" class=\"data row40 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row40_col6\" class=\"data row40 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row41_col0\" class=\"data row41 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
-              "      <td id=\"T_05adb_row41_col1\" class=\"data row41 col1\" >Confusion Matrix Drift</td>\n",
-              "      <td id=\"T_05adb_row41_col2\" class=\"data row41 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_05adb_row41_col3\" class=\"data row41 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row41_col4\" class=\"data row41 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_05adb_row41_col5\" class=\"data row41 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_05adb_row41_col6\" class=\"data row41 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_05adb_row42_col0\" class=\"data row42 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
-              "      <td id=\"T_05adb_row42_col1\" class=\"data row42 col1\" >ROC Curve Drift</td>\n",
-              "      <td id=\"T_05adb_row42_col2\" class=\"data row42 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_05adb_row42_col3\" class=\"data row42 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_05adb_row42_col4\" class=\"data row42 col4\" >{}</td>\n",
-              "      <td id=\"T_05adb_row42_col5\" class=\"data row42 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_05adb_row42_col6\" class=\"data row42 col6\" >['classification', 'text_classification']</td>\n",
+              "      <td id=\"T_326c3_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
+              "      <td id=\"T_326c3_row0_col1\" class=\"data row0 col1\" >Cluster Size Distribution</td>\n",
+              "      <td id=\"T_326c3_row0_col2\" class=\"data row0 col2\" >Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...</td>\n",
+              "      <td id=\"T_326c3_row0_col3\" class=\"data row0 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row0_col4\" class=\"data row0 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row0_col5\" class=\"data row0 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row0_col6\" class=\"data row0 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row0_col7\" class=\"data row0 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row0_col8\" class=\"data row0 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
+              "      <td id=\"T_326c3_row1_col1\" class=\"data row1 col1\" >Time Series R2 Square By Segments</td>\n",
+              "      <td id=\"T_326c3_row1_col2\" class=\"data row1 col2\" >Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...</td>\n",
+              "      <td id=\"T_326c3_row1_col3\" class=\"data row1 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row1_col4\" class=\"data row1 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row1_col5\" class=\"data row1 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row1_col6\" class=\"data row1 col6\" >{'segments': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row1_col7\" class=\"data row1 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_326c3_row1_col8\" class=\"data row1 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
+              "      <td id=\"T_326c3_row2_col1\" class=\"data row2 col1\" >Adjusted Mutual Information</td>\n",
+              "      <td id=\"T_326c3_row2_col2\" class=\"data row2 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
+              "      <td id=\"T_326c3_row2_col3\" class=\"data row2 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row2_col4\" class=\"data row2 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row2_col5\" class=\"data row2 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row2_col6\" class=\"data row2 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row2_col7\" class=\"data row2 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_326c3_row2_col8\" class=\"data row2 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
+              "      <td id=\"T_326c3_row3_col1\" class=\"data row3 col1\" >Adjusted Rand Index</td>\n",
+              "      <td id=\"T_326c3_row3_col2\" class=\"data row3 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
+              "      <td id=\"T_326c3_row3_col3\" class=\"data row3 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row3_col4\" class=\"data row3 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row3_col5\" class=\"data row3 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row3_col6\" class=\"data row3 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row3_col7\" class=\"data row3 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_326c3_row3_col8\" class=\"data row3 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
+              "      <td id=\"T_326c3_row4_col1\" class=\"data row4 col1\" >Calibration Curve</td>\n",
+              "      <td id=\"T_326c3_row4_col2\" class=\"data row4 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
+              "      <td id=\"T_326c3_row4_col3\" class=\"data row4 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row4_col4\" class=\"data row4 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row4_col5\" class=\"data row4 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row4_col6\" class=\"data row4 col6\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_326c3_row4_col7\" class=\"data row4 col7\" >['sklearn', 'model_performance', 'classification']</td>\n",
+              "      <td id=\"T_326c3_row4_col8\" class=\"data row4 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row5_col0\" class=\"data row5 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
+              "      <td id=\"T_326c3_row5_col1\" class=\"data row5 col1\" >Classifier Performance</td>\n",
+              "      <td id=\"T_326c3_row5_col2\" class=\"data row5 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
+              "      <td id=\"T_326c3_row5_col3\" class=\"data row5 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row5_col4\" class=\"data row5 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row5_col5\" class=\"data row5 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row5_col6\" class=\"data row5 col6\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
+              "      <td id=\"T_326c3_row5_col7\" class=\"data row5 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row5_col8\" class=\"data row5 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row6_col0\" class=\"data row6 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
+              "      <td id=\"T_326c3_row6_col1\" class=\"data row6 col1\" >Classifier Threshold Optimization</td>\n",
+              "      <td id=\"T_326c3_row6_col2\" class=\"data row6 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
+              "      <td id=\"T_326c3_row6_col3\" class=\"data row6 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row6_col4\" class=\"data row6 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row6_col5\" class=\"data row6 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row6_col6\" class=\"data row6 col6\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row6_col7\" class=\"data row6 col7\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
+              "      <td id=\"T_326c3_row6_col8\" class=\"data row6 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
+              "      <td id=\"T_326c3_row7_col1\" class=\"data row7 col1\" >Cluster Cosine Similarity</td>\n",
+              "      <td id=\"T_326c3_row7_col2\" class=\"data row7 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
+              "      <td id=\"T_326c3_row7_col3\" class=\"data row7 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row7_col4\" class=\"data row7 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row7_col5\" class=\"data row7 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row7_col6\" class=\"data row7 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row7_col7\" class=\"data row7 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_326c3_row7_col8\" class=\"data row7 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
+              "      <td id=\"T_326c3_row8_col1\" class=\"data row8 col1\" >Cluster Performance Metrics</td>\n",
+              "      <td id=\"T_326c3_row8_col2\" class=\"data row8 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
+              "      <td id=\"T_326c3_row8_col3\" class=\"data row8 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row8_col4\" class=\"data row8 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row8_col5\" class=\"data row8 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row8_col6\" class=\"data row8 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row8_col7\" class=\"data row8 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_326c3_row8_col8\" class=\"data row8 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
+              "      <td id=\"T_326c3_row9_col1\" class=\"data row9 col1\" >Completeness Score</td>\n",
+              "      <td id=\"T_326c3_row9_col2\" class=\"data row9 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
+              "      <td id=\"T_326c3_row9_col3\" class=\"data row9 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row9_col4\" class=\"data row9 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row9_col5\" class=\"data row9 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row9_col6\" class=\"data row9 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row9_col7\" class=\"data row9 col7\" >['sklearn', 'model_performance', 'clustering']</td>\n",
+              "      <td id=\"T_326c3_row9_col8\" class=\"data row9 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
+              "      <td id=\"T_326c3_row10_col1\" class=\"data row10 col1\" >Confusion Matrix</td>\n",
+              "      <td id=\"T_326c3_row10_col2\" class=\"data row10 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
+              "      <td id=\"T_326c3_row10_col3\" class=\"data row10 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row10_col4\" class=\"data row10 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row10_col5\" class=\"data row10 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row10_col6\" class=\"data row10 col6\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_326c3_row10_col7\" class=\"data row10 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row10_col8\" class=\"data row10 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.sklearn.FeatureImportance</td>\n",
+              "      <td id=\"T_326c3_row11_col1\" class=\"data row11 col1\" >Feature Importance</td>\n",
+              "      <td id=\"T_326c3_row11_col2\" class=\"data row11 col2\" >Compute feature importance scores for a given model and generate a summary table...</td>\n",
+              "      <td id=\"T_326c3_row11_col3\" class=\"data row11 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row11_col4\" class=\"data row11 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row11_col5\" class=\"data row11 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row11_col6\" class=\"data row11 col6\" >{'num_features': {'type': 'int', 'default': 3}}</td>\n",
+              "      <td id=\"T_326c3_row11_col7\" class=\"data row11 col7\" >['model_explainability', 'sklearn']</td>\n",
+              "      <td id=\"T_326c3_row11_col8\" class=\"data row11 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
+              "      <td id=\"T_326c3_row12_col1\" class=\"data row12 col1\" >Fowlkes Mallows Score</td>\n",
+              "      <td id=\"T_326c3_row12_col2\" class=\"data row12 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
+              "      <td id=\"T_326c3_row12_col3\" class=\"data row12 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row12_col4\" class=\"data row12 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row12_col5\" class=\"data row12 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row12_col6\" class=\"data row12 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row12_col7\" class=\"data row12 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row12_col8\" class=\"data row12 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
+              "      <td id=\"T_326c3_row13_col1\" class=\"data row13 col1\" >Homogeneity Score</td>\n",
+              "      <td id=\"T_326c3_row13_col2\" class=\"data row13 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
+              "      <td id=\"T_326c3_row13_col3\" class=\"data row13 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row13_col4\" class=\"data row13 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row13_col5\" class=\"data row13 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row13_col6\" class=\"data row13 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row13_col7\" class=\"data row13 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row13_col8\" class=\"data row13 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
+              "      <td id=\"T_326c3_row14_col1\" class=\"data row14 col1\" >Hyper Parameters Tuning</td>\n",
+              "      <td id=\"T_326c3_row14_col2\" class=\"data row14 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
+              "      <td id=\"T_326c3_row14_col3\" class=\"data row14 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row14_col4\" class=\"data row14 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row14_col5\" class=\"data row14 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row14_col6\" class=\"data row14 col6\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row14_col7\" class=\"data row14 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row14_col8\" class=\"data row14 col8\" >['clustering', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
+              "      <td id=\"T_326c3_row15_col1\" class=\"data row15 col1\" >K Means Clusters Optimization</td>\n",
+              "      <td id=\"T_326c3_row15_col2\" class=\"data row15 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
+              "      <td id=\"T_326c3_row15_col3\" class=\"data row15 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row15_col4\" class=\"data row15 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row15_col5\" class=\"data row15 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row15_col6\" class=\"data row15 col6\" >{'n_clusters': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row15_col7\" class=\"data row15 col7\" >['sklearn', 'model_performance', 'kmeans']</td>\n",
+              "      <td id=\"T_326c3_row15_col8\" class=\"data row15 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
+              "      <td id=\"T_326c3_row16_col1\" class=\"data row16 col1\" >Minimum Accuracy</td>\n",
+              "      <td id=\"T_326c3_row16_col2\" class=\"data row16 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_326c3_row16_col3\" class=\"data row16 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row16_col4\" class=\"data row16 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row16_col5\" class=\"data row16 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row16_col6\" class=\"data row16 col6\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_326c3_row16_col7\" class=\"data row16 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row16_col8\" class=\"data row16 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
+              "      <td id=\"T_326c3_row17_col1\" class=\"data row17 col1\" >Minimum F1 Score</td>\n",
+              "      <td id=\"T_326c3_row17_col2\" class=\"data row17 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
+              "      <td id=\"T_326c3_row17_col3\" class=\"data row17 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row17_col4\" class=\"data row17 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row17_col5\" class=\"data row17 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row17_col6\" class=\"data row17 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_326c3_row17_col7\" class=\"data row17 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row17_col8\" class=\"data row17 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
+              "      <td id=\"T_326c3_row18_col1\" class=\"data row18 col1\" >Minimum ROCAUC Score</td>\n",
+              "      <td id=\"T_326c3_row18_col2\" class=\"data row18 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_326c3_row18_col3\" class=\"data row18 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row18_col4\" class=\"data row18 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row18_col5\" class=\"data row18 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row18_col6\" class=\"data row18 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_326c3_row18_col7\" class=\"data row18 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row18_col8\" class=\"data row18 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
+              "      <td id=\"T_326c3_row19_col1\" class=\"data row19 col1\" >Model Parameters</td>\n",
+              "      <td id=\"T_326c3_row19_col2\" class=\"data row19 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
+              "      <td id=\"T_326c3_row19_col3\" class=\"data row19 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row19_col4\" class=\"data row19 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row19_col5\" class=\"data row19 col5\" >['model']</td>\n",
+              "      <td id=\"T_326c3_row19_col6\" class=\"data row19 col6\" >{'model_params': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row19_col7\" class=\"data row19 col7\" >['model_training', 'metadata']</td>\n",
+              "      <td id=\"T_326c3_row19_col8\" class=\"data row19 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
+              "      <td id=\"T_326c3_row20_col1\" class=\"data row20 col1\" >Models Performance Comparison</td>\n",
+              "      <td id=\"T_326c3_row20_col2\" class=\"data row20 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
+              "      <td id=\"T_326c3_row20_col3\" class=\"data row20 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row20_col4\" class=\"data row20 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row20_col5\" class=\"data row20 col5\" >['dataset', 'models']</td>\n",
+              "      <td id=\"T_326c3_row20_col6\" class=\"data row20 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row20_col7\" class=\"data row20 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
+              "      <td id=\"T_326c3_row20_col8\" class=\"data row20 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
+              "      <td id=\"T_326c3_row21_col1\" class=\"data row21 col1\" >Overfit Diagnosis</td>\n",
+              "      <td id=\"T_326c3_row21_col2\" class=\"data row21 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
+              "      <td id=\"T_326c3_row21_col3\" class=\"data row21 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row21_col4\" class=\"data row21 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row21_col5\" class=\"data row21 col5\" >['model', 'datasets']</td>\n",
+              "      <td id=\"T_326c3_row21_col6\" class=\"data row21 col6\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
+              "      <td id=\"T_326c3_row21_col7\" class=\"data row21 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
+              "      <td id=\"T_326c3_row21_col8\" class=\"data row21 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
+              "      <td id=\"T_326c3_row22_col1\" class=\"data row22 col1\" >Permutation Feature Importance</td>\n",
+              "      <td id=\"T_326c3_row22_col2\" class=\"data row22 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
+              "      <td id=\"T_326c3_row22_col3\" class=\"data row22 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row22_col4\" class=\"data row22 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row22_col5\" class=\"data row22 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row22_col6\" class=\"data row22 col6\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row22_col7\" class=\"data row22 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row22_col8\" class=\"data row22 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
+              "      <td id=\"T_326c3_row23_col1\" class=\"data row23 col1\" >Population Stability Index</td>\n",
+              "      <td id=\"T_326c3_row23_col2\" class=\"data row23 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
+              "      <td id=\"T_326c3_row23_col3\" class=\"data row23 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row23_col4\" class=\"data row23 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row23_col5\" class=\"data row23 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row23_col6\" class=\"data row23 col6\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
+              "      <td id=\"T_326c3_row23_col7\" class=\"data row23 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row23_col8\" class=\"data row23 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
+              "      <td id=\"T_326c3_row24_col1\" class=\"data row24 col1\" >Precision Recall Curve</td>\n",
+              "      <td id=\"T_326c3_row24_col2\" class=\"data row24 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
+              "      <td id=\"T_326c3_row24_col3\" class=\"data row24 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row24_col4\" class=\"data row24 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row24_col5\" class=\"data row24 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row24_col6\" class=\"data row24 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row24_col7\" class=\"data row24 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row24_col8\" class=\"data row24 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
+              "      <td id=\"T_326c3_row25_col1\" class=\"data row25 col1\" >ROC Curve</td>\n",
+              "      <td id=\"T_326c3_row25_col2\" class=\"data row25 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
+              "      <td id=\"T_326c3_row25_col3\" class=\"data row25 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row25_col4\" class=\"data row25 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row25_col5\" class=\"data row25 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row25_col6\" class=\"data row25 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row25_col7\" class=\"data row25 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row25_col8\" class=\"data row25 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
+              "      <td id=\"T_326c3_row26_col1\" class=\"data row26 col1\" >Regression Errors</td>\n",
+              "      <td id=\"T_326c3_row26_col2\" class=\"data row26 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
+              "      <td id=\"T_326c3_row26_col3\" class=\"data row26 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row26_col4\" class=\"data row26 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row26_col5\" class=\"data row26 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row26_col6\" class=\"data row26 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row26_col7\" class=\"data row26 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row26_col8\" class=\"data row26 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
+              "      <td id=\"T_326c3_row27_col1\" class=\"data row27 col1\" >Regression Errors Comparison</td>\n",
+              "      <td id=\"T_326c3_row27_col2\" class=\"data row27 col2\" >Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...</td>\n",
+              "      <td id=\"T_326c3_row27_col3\" class=\"data row27 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row27_col4\" class=\"data row27 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row27_col5\" class=\"data row27 col5\" >['datasets', 'models']</td>\n",
+              "      <td id=\"T_326c3_row27_col6\" class=\"data row27 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row27_col7\" class=\"data row27 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_326c3_row27_col8\" class=\"data row27 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.sklearn.RegressionPerformance</td>\n",
+              "      <td id=\"T_326c3_row28_col1\" class=\"data row28 col1\" >Regression Performance</td>\n",
+              "      <td id=\"T_326c3_row28_col2\" class=\"data row28 col2\" >Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....</td>\n",
+              "      <td id=\"T_326c3_row28_col3\" class=\"data row28 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row28_col4\" class=\"data row28 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row28_col5\" class=\"data row28 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row28_col6\" class=\"data row28 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row28_col7\" class=\"data row28 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row28_col8\" class=\"data row28 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
+              "      <td id=\"T_326c3_row29_col1\" class=\"data row29 col1\" >Regression R2 Square</td>\n",
+              "      <td id=\"T_326c3_row29_col2\" class=\"data row29 col2\" >Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...</td>\n",
+              "      <td id=\"T_326c3_row29_col3\" class=\"data row29 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row29_col4\" class=\"data row29 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row29_col5\" class=\"data row29 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row29_col6\" class=\"data row29 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row29_col7\" class=\"data row29 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row29_col8\" class=\"data row29 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
+              "      <td id=\"T_326c3_row30_col1\" class=\"data row30 col1\" >Regression R2 Square Comparison</td>\n",
+              "      <td id=\"T_326c3_row30_col2\" class=\"data row30 col2\" >Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...</td>\n",
+              "      <td id=\"T_326c3_row30_col3\" class=\"data row30 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row30_col4\" class=\"data row30 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row30_col5\" class=\"data row30 col5\" >['datasets', 'models']</td>\n",
+              "      <td id=\"T_326c3_row30_col6\" class=\"data row30 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row30_col7\" class=\"data row30 col7\" >['model_performance', 'sklearn']</td>\n",
+              "      <td id=\"T_326c3_row30_col8\" class=\"data row30 col8\" >['regression', 'time_series_forecasting']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
+              "      <td id=\"T_326c3_row31_col1\" class=\"data row31 col1\" >Robustness Diagnosis</td>\n",
+              "      <td id=\"T_326c3_row31_col2\" class=\"data row31 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
+              "      <td id=\"T_326c3_row31_col3\" class=\"data row31 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row31_col4\" class=\"data row31 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row31_col5\" class=\"data row31 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row31_col6\" class=\"data row31 col6\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_326c3_row31_col7\" class=\"data row31 col7\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row31_col8\" class=\"data row31 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
+              "      <td id=\"T_326c3_row32_col1\" class=\"data row32 col1\" >SHAP Global Importance</td>\n",
+              "      <td id=\"T_326c3_row32_col2\" class=\"data row32 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
+              "      <td id=\"T_326c3_row32_col3\" class=\"data row32 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row32_col4\" class=\"data row32 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row32_col5\" class=\"data row32 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row32_col6\" class=\"data row32 col6\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row32_col7\" class=\"data row32 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row32_col8\" class=\"data row32 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
+              "      <td id=\"T_326c3_row33_col1\" class=\"data row33 col1\" >Score Probability Alignment</td>\n",
+              "      <td id=\"T_326c3_row33_col2\" class=\"data row33 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
+              "      <td id=\"T_326c3_row33_col3\" class=\"data row33 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row33_col4\" class=\"data row33 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row33_col5\" class=\"data row33 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row33_col6\" class=\"data row33 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_326c3_row33_col7\" class=\"data row33 col7\" >['visualization', 'credit_risk', 'calibration']</td>\n",
+              "      <td id=\"T_326c3_row33_col8\" class=\"data row33 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
+              "      <td id=\"T_326c3_row34_col1\" class=\"data row34 col1\" >Silhouette Plot</td>\n",
+              "      <td id=\"T_326c3_row34_col2\" class=\"data row34 col2\" >Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...</td>\n",
+              "      <td id=\"T_326c3_row34_col3\" class=\"data row34 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row34_col4\" class=\"data row34 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row34_col5\" class=\"data row34 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_326c3_row34_col6\" class=\"data row34 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row34_col7\" class=\"data row34 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row34_col8\" class=\"data row34 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
+              "      <td id=\"T_326c3_row35_col1\" class=\"data row35 col1\" >Training Test Degradation</td>\n",
+              "      <td id=\"T_326c3_row35_col2\" class=\"data row35 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
+              "      <td id=\"T_326c3_row35_col3\" class=\"data row35 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row35_col4\" class=\"data row35 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row35_col5\" class=\"data row35 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row35_col6\" class=\"data row35 col6\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_326c3_row35_col7\" class=\"data row35 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row35_col8\" class=\"data row35 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
+              "      <td id=\"T_326c3_row36_col1\" class=\"data row36 col1\" >V Measure</td>\n",
+              "      <td id=\"T_326c3_row36_col2\" class=\"data row36 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
+              "      <td id=\"T_326c3_row36_col3\" class=\"data row36 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row36_col4\" class=\"data row36 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row36_col5\" class=\"data row36 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_326c3_row36_col6\" class=\"data row36 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row36_col7\" class=\"data row36 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row36_col8\" class=\"data row36 col8\" >['clustering']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
+              "      <td id=\"T_326c3_row37_col1\" class=\"data row37 col1\" >Weakspots Diagnosis</td>\n",
+              "      <td id=\"T_326c3_row37_col2\" class=\"data row37 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
+              "      <td id=\"T_326c3_row37_col3\" class=\"data row37 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row37_col4\" class=\"data row37 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row37_col5\" class=\"data row37 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row37_col6\" class=\"data row37 col6\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_326c3_row37_col7\" class=\"data row37 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row37_col8\" class=\"data row37 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row38_col0\" class=\"data row38 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
+              "      <td id=\"T_326c3_row38_col1\" class=\"data row38 col1\" >Calibration Curve Drift</td>\n",
+              "      <td id=\"T_326c3_row38_col2\" class=\"data row38 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_326c3_row38_col3\" class=\"data row38 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row38_col4\" class=\"data row38 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row38_col5\" class=\"data row38 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row38_col6\" class=\"data row38 col6\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_326c3_row38_col7\" class=\"data row38 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row38_col8\" class=\"data row38 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row39_col0\" class=\"data row39 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
+              "      <td id=\"T_326c3_row39_col1\" class=\"data row39 col1\" >Class Discrimination Drift</td>\n",
+              "      <td id=\"T_326c3_row39_col2\" class=\"data row39 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_326c3_row39_col3\" class=\"data row39 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row39_col4\" class=\"data row39 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row39_col5\" class=\"data row39 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row39_col6\" class=\"data row39 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_326c3_row39_col7\" class=\"data row39 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row39_col8\" class=\"data row39 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row40_col0\" class=\"data row40 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
+              "      <td id=\"T_326c3_row40_col1\" class=\"data row40 col1\" >Classification Accuracy Drift</td>\n",
+              "      <td id=\"T_326c3_row40_col2\" class=\"data row40 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_326c3_row40_col3\" class=\"data row40 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row40_col4\" class=\"data row40 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row40_col5\" class=\"data row40 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row40_col6\" class=\"data row40 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_326c3_row40_col7\" class=\"data row40 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row40_col8\" class=\"data row40 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row41_col0\" class=\"data row41 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
+              "      <td id=\"T_326c3_row41_col1\" class=\"data row41 col1\" >Confusion Matrix Drift</td>\n",
+              "      <td id=\"T_326c3_row41_col2\" class=\"data row41 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_326c3_row41_col3\" class=\"data row41 col3\" >False</td>\n",
+              "      <td id=\"T_326c3_row41_col4\" class=\"data row41 col4\" >True</td>\n",
+              "      <td id=\"T_326c3_row41_col5\" class=\"data row41 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row41_col6\" class=\"data row41 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_326c3_row41_col7\" class=\"data row41 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_326c3_row41_col8\" class=\"data row41 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_326c3_row42_col0\" class=\"data row42 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
+              "      <td id=\"T_326c3_row42_col1\" class=\"data row42 col1\" >ROC Curve Drift</td>\n",
+              "      <td id=\"T_326c3_row42_col2\" class=\"data row42 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_326c3_row42_col3\" class=\"data row42 col3\" >True</td>\n",
+              "      <td id=\"T_326c3_row42_col4\" class=\"data row42 col4\" >False</td>\n",
+              "      <td id=\"T_326c3_row42_col5\" class=\"data row42 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_326c3_row42_col6\" class=\"data row42 col6\" >{}</td>\n",
+              "      <td id=\"T_326c3_row42_col7\" class=\"data row42 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_326c3_row42_col8\" class=\"data row42 col8\" >['classification', 'text_classification']</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x3618a9580>"
+              "<pandas.io.formats.style.Styler at 0x1052e6790>"
             ]
           },
           "execution_count": 6,
@@ -2585,715 +3065,869 @@
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               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_686e6\">\n",
+              "<table id=\"T_56dd5\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_686e6_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_686e6_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_686e6_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_686e6_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_686e6_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
-              "      <th id=\"T_686e6_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
-              "      <th id=\"T_686e6_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
+              "      <th id=\"T_56dd5_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
+              "      <th id=\"T_56dd5_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
+              "      <th id=\"T_56dd5_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+              "      <th id=\"T_56dd5_level0_col3\" class=\"col_heading level0 col3\" >Has Figure</th>\n",
+              "      <th id=\"T_56dd5_level0_col4\" class=\"col_heading level0 col4\" >Has Table</th>\n",
+              "      <th id=\"T_56dd5_level0_col5\" class=\"col_heading level0 col5\" >Required Inputs</th>\n",
+              "      <th id=\"T_56dd5_level0_col6\" class=\"col_heading level0 col6\" >Params</th>\n",
+              "      <th id=\"T_56dd5_level0_col7\" class=\"col_heading level0 col7\" >Tags</th>\n",
+              "      <th id=\"T_56dd5_level0_col8\" class=\"col_heading level0 col8\" >Tasks</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_686e6_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
-              "      <td id=\"T_686e6_row0_col1\" class=\"data row0 col1\" >Bivariate Scatter Plots</td>\n",
-              "      <td id=\"T_686e6_row0_col2\" class=\"data row0 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
-              "      <td id=\"T_686e6_row0_col3\" class=\"data row0 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row0_col4\" class=\"data row0 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row0_col5\" class=\"data row0 col5\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row0_col6\" class=\"data row0 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
-              "      <td id=\"T_686e6_row1_col1\" class=\"data row1 col1\" >Chi Squared Features Table</td>\n",
-              "      <td id=\"T_686e6_row1_col2\" class=\"data row1 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
-              "      <td id=\"T_686e6_row1_col3\" class=\"data row1 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row1_col4\" class=\"data row1 col4\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_686e6_row1_col5\" class=\"data row1 col5\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
-              "      <td id=\"T_686e6_row1_col6\" class=\"data row1 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.ClassImbalance</td>\n",
-              "      <td id=\"T_686e6_row2_col1\" class=\"data row2 col1\" >Class Imbalance</td>\n",
-              "      <td id=\"T_686e6_row2_col2\" class=\"data row2 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
-              "      <td id=\"T_686e6_row2_col3\" class=\"data row2 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row2_col4\" class=\"data row2 col4\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_686e6_row2_col5\" class=\"data row2 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
-              "      <td id=\"T_686e6_row2_col6\" class=\"data row2 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.DatasetDescription</td>\n",
-              "      <td id=\"T_686e6_row3_col1\" class=\"data row3 col1\" >Dataset Description</td>\n",
-              "      <td id=\"T_686e6_row3_col2\" class=\"data row3 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
-              "      <td id=\"T_686e6_row3_col3\" class=\"data row3 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row3_col5\" class=\"data row3 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
-              "      <td id=\"T_686e6_row3_col6\" class=\"data row3 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.DatasetSplit</td>\n",
-              "      <td id=\"T_686e6_row4_col1\" class=\"data row4 col1\" >Dataset Split</td>\n",
-              "      <td id=\"T_686e6_row4_col2\" class=\"data row4 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
-              "      <td id=\"T_686e6_row4_col3\" class=\"data row4 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_686e6_row4_col4\" class=\"data row4 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row4_col5\" class=\"data row4 col5\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
-              "      <td id=\"T_686e6_row4_col6\" class=\"data row4 col6\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
-              "      <td id=\"T_686e6_row5_col1\" class=\"data row5 col1\" >Descriptive Statistics</td>\n",
-              "      <td id=\"T_686e6_row5_col2\" class=\"data row5 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
-              "      <td id=\"T_686e6_row5_col3\" class=\"data row5 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row5_col5\" class=\"data row5 col5\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
-              "      <td id=\"T_686e6_row5_col6\" class=\"data row5 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.Duplicates</td>\n",
-              "      <td id=\"T_686e6_row6_col1\" class=\"data row6 col1\" >Duplicates</td>\n",
-              "      <td id=\"T_686e6_row6_col2\" class=\"data row6 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
-              "      <td id=\"T_686e6_row6_col3\" class=\"data row6 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row6_col4\" class=\"data row6 col4\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
-              "      <td id=\"T_686e6_row6_col5\" class=\"data row6 col5\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
-              "      <td id=\"T_686e6_row6_col6\" class=\"data row6 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
-              "      <td id=\"T_686e6_row7_col1\" class=\"data row7 col1\" >Feature Target Correlation Plot</td>\n",
-              "      <td id=\"T_686e6_row7_col2\" class=\"data row7 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
-              "      <td id=\"T_686e6_row7_col3\" class=\"data row7 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row7_col4\" class=\"data row7 col4\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
-              "      <td id=\"T_686e6_row7_col5\" class=\"data row7 col5\" >['tabular_data', 'visualization', 'correlation']</td>\n",
-              "      <td id=\"T_686e6_row7_col6\" class=\"data row7 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.HighCardinality</td>\n",
-              "      <td id=\"T_686e6_row8_col1\" class=\"data row8 col1\" >High Cardinality</td>\n",
-              "      <td id=\"T_686e6_row8_col2\" class=\"data row8 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
-              "      <td id=\"T_686e6_row8_col3\" class=\"data row8 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row8_col4\" class=\"data row8 col4\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
-              "      <td id=\"T_686e6_row8_col5\" class=\"data row8 col5\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
-              "      <td id=\"T_686e6_row8_col6\" class=\"data row8 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
-              "      <td id=\"T_686e6_row9_col1\" class=\"data row9 col1\" >High Pearson Correlation</td>\n",
-              "      <td id=\"T_686e6_row9_col2\" class=\"data row9 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
-              "      <td id=\"T_686e6_row9_col3\" class=\"data row9 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row9_col4\" class=\"data row9 col4\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row9_col5\" class=\"data row9 col5\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
-              "      <td id=\"T_686e6_row9_col6\" class=\"data row9 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
-              "      <td id=\"T_686e6_row10_col1\" class=\"data row10 col1\" >IQR Outliers Bar Plot</td>\n",
-              "      <td id=\"T_686e6_row10_col2\" class=\"data row10 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
-              "      <td id=\"T_686e6_row10_col3\" class=\"data row10 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row10_col4\" class=\"data row10 col4\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
-              "      <td id=\"T_686e6_row10_col5\" class=\"data row10 col5\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
-              "      <td id=\"T_686e6_row10_col6\" class=\"data row10 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
-              "      <td id=\"T_686e6_row11_col1\" class=\"data row11 col1\" >IQR Outliers Table</td>\n",
-              "      <td id=\"T_686e6_row11_col2\" class=\"data row11 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
-              "      <td id=\"T_686e6_row11_col3\" class=\"data row11 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row11_col4\" class=\"data row11 col4\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
-              "      <td id=\"T_686e6_row11_col5\" class=\"data row11 col5\" >['tabular_data', 'numerical_data']</td>\n",
-              "      <td id=\"T_686e6_row11_col6\" class=\"data row11 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
-              "      <td id=\"T_686e6_row12_col1\" class=\"data row12 col1\" >Isolation Forest Outliers</td>\n",
-              "      <td id=\"T_686e6_row12_col2\" class=\"data row12 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
-              "      <td id=\"T_686e6_row12_col3\" class=\"data row12 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row12_col4\" class=\"data row12 col4\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row12_col5\" class=\"data row12 col5\" >['tabular_data', 'anomaly_detection']</td>\n",
-              "      <td id=\"T_686e6_row12_col6\" class=\"data row12 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.JarqueBera</td>\n",
-              "      <td id=\"T_686e6_row13_col1\" class=\"data row13 col1\" >Jarque Bera</td>\n",
-              "      <td id=\"T_686e6_row13_col2\" class=\"data row13 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
-              "      <td id=\"T_686e6_row13_col3\" class=\"data row13 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row13_col4\" class=\"data row13 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row13_col5\" class=\"data row13 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_686e6_row13_col6\" class=\"data row13 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.MissingValues</td>\n",
-              "      <td id=\"T_686e6_row14_col1\" class=\"data row14 col1\" >Missing Values</td>\n",
-              "      <td id=\"T_686e6_row14_col2\" class=\"data row14 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
-              "      <td id=\"T_686e6_row14_col3\" class=\"data row14 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row14_col4\" class=\"data row14 col4\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
-              "      <td id=\"T_686e6_row14_col5\" class=\"data row14 col5\" >['tabular_data', 'data_quality']</td>\n",
-              "      <td id=\"T_686e6_row14_col6\" class=\"data row14 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
-              "      <td id=\"T_686e6_row15_col1\" class=\"data row15 col1\" >Missing Values Bar Plot</td>\n",
-              "      <td id=\"T_686e6_row15_col2\" class=\"data row15 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
-              "      <td id=\"T_686e6_row15_col3\" class=\"data row15 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row15_col4\" class=\"data row15 col4\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
-              "      <td id=\"T_686e6_row15_col5\" class=\"data row15 col5\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row15_col6\" class=\"data row15 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.MutualInformation</td>\n",
-              "      <td id=\"T_686e6_row16_col1\" class=\"data row16 col1\" >Mutual Information</td>\n",
-              "      <td id=\"T_686e6_row16_col2\" class=\"data row16 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
-              "      <td id=\"T_686e6_row16_col3\" class=\"data row16 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row16_col4\" class=\"data row16 col4\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
-              "      <td id=\"T_686e6_row16_col5\" class=\"data row16 col5\" >['feature_selection', 'data_analysis']</td>\n",
-              "      <td id=\"T_686e6_row16_col6\" class=\"data row16 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
-              "      <td id=\"T_686e6_row17_col1\" class=\"data row17 col1\" >Pearson Correlation Matrix</td>\n",
-              "      <td id=\"T_686e6_row17_col2\" class=\"data row17 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
-              "      <td id=\"T_686e6_row17_col3\" class=\"data row17 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row17_col4\" class=\"data row17 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row17_col5\" class=\"data row17 col5\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
-              "      <td id=\"T_686e6_row17_col6\" class=\"data row17 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
-              "      <td id=\"T_686e6_row18_col1\" class=\"data row18 col1\" >Protected Classes Description</td>\n",
-              "      <td id=\"T_686e6_row18_col2\" class=\"data row18 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
-              "      <td id=\"T_686e6_row18_col3\" class=\"data row18 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row18_col4\" class=\"data row18 col4\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row18_col5\" class=\"data row18 col5\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
-              "      <td id=\"T_686e6_row18_col6\" class=\"data row18 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.RunsTest</td>\n",
-              "      <td id=\"T_686e6_row19_col1\" class=\"data row19 col1\" >Runs Test</td>\n",
-              "      <td id=\"T_686e6_row19_col2\" class=\"data row19 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
-              "      <td id=\"T_686e6_row19_col3\" class=\"data row19 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row19_col4\" class=\"data row19 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row19_col5\" class=\"data row19 col5\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_686e6_row19_col6\" class=\"data row19 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.ScatterPlot</td>\n",
-              "      <td id=\"T_686e6_row20_col1\" class=\"data row20 col1\" >Scatter Plot</td>\n",
-              "      <td id=\"T_686e6_row20_col2\" class=\"data row20 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
-              "      <td id=\"T_686e6_row20_col3\" class=\"data row20 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row20_col4\" class=\"data row20 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row20_col5\" class=\"data row20 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row20_col6\" class=\"data row20 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
-              "      <td id=\"T_686e6_row21_col1\" class=\"data row21 col1\" >Score Band Default Rates</td>\n",
-              "      <td id=\"T_686e6_row21_col2\" class=\"data row21 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
-              "      <td id=\"T_686e6_row21_col3\" class=\"data row21 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row21_col4\" class=\"data row21 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row21_col5\" class=\"data row21 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
-              "      <td id=\"T_686e6_row21_col6\" class=\"data row21 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
-              "      <td id=\"T_686e6_row22_col1\" class=\"data row22 col1\" >Shapiro Wilk</td>\n",
-              "      <td id=\"T_686e6_row22_col2\" class=\"data row22 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
-              "      <td id=\"T_686e6_row22_col3\" class=\"data row22 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row22_col4\" class=\"data row22 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row22_col5\" class=\"data row22 col5\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
-              "      <td id=\"T_686e6_row22_col6\" class=\"data row22 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.Skewness</td>\n",
-              "      <td id=\"T_686e6_row23_col1\" class=\"data row23 col1\" >Skewness</td>\n",
-              "      <td id=\"T_686e6_row23_col2\" class=\"data row23 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
-              "      <td id=\"T_686e6_row23_col3\" class=\"data row23 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row23_col4\" class=\"data row23 col4\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
-              "      <td id=\"T_686e6_row23_col5\" class=\"data row23 col5\" >['data_quality', 'tabular_data']</td>\n",
-              "      <td id=\"T_686e6_row23_col6\" class=\"data row23 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
-              "      <td id=\"T_686e6_row24_col1\" class=\"data row24 col1\" >Tabular Categorical Bar Plots</td>\n",
-              "      <td id=\"T_686e6_row24_col2\" class=\"data row24 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
-              "      <td id=\"T_686e6_row24_col3\" class=\"data row24 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row24_col4\" class=\"data row24 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row24_col5\" class=\"data row24 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row24_col6\" class=\"data row24 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
-              "      <td id=\"T_686e6_row25_col1\" class=\"data row25 col1\" >Tabular Date Time Histograms</td>\n",
-              "      <td id=\"T_686e6_row25_col2\" class=\"data row25 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
-              "      <td id=\"T_686e6_row25_col3\" class=\"data row25 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row25_col4\" class=\"data row25 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row25_col5\" class=\"data row25 col5\" >['time_series_data', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row25_col6\" class=\"data row25 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
-              "      <td id=\"T_686e6_row26_col1\" class=\"data row26 col1\" >Tabular Description Tables</td>\n",
-              "      <td id=\"T_686e6_row26_col2\" class=\"data row26 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
-              "      <td id=\"T_686e6_row26_col3\" class=\"data row26 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row26_col4\" class=\"data row26 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row26_col5\" class=\"data row26 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_686e6_row26_col6\" class=\"data row26 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
-              "      <td id=\"T_686e6_row27_col1\" class=\"data row27 col1\" >Tabular Numerical Histograms</td>\n",
-              "      <td id=\"T_686e6_row27_col2\" class=\"data row27 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
-              "      <td id=\"T_686e6_row27_col3\" class=\"data row27 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row27_col5\" class=\"data row27 col5\" >['tabular_data', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row27_col6\" class=\"data row27 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
-              "      <td id=\"T_686e6_row28_col1\" class=\"data row28 col1\" >Target Rate Bar Plots</td>\n",
-              "      <td id=\"T_686e6_row28_col2\" class=\"data row28 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
-              "      <td id=\"T_686e6_row28_col3\" class=\"data row28 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row28_col4\" class=\"data row28 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row28_col5\" class=\"data row28 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
-              "      <td id=\"T_686e6_row28_col6\" class=\"data row28 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
-              "      <td id=\"T_686e6_row29_col1\" class=\"data row29 col1\" >Too Many Zero Values</td>\n",
-              "      <td id=\"T_686e6_row29_col2\" class=\"data row29 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
-              "      <td id=\"T_686e6_row29_col3\" class=\"data row29 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row29_col4\" class=\"data row29 col4\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
-              "      <td id=\"T_686e6_row29_col5\" class=\"data row29 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_686e6_row29_col6\" class=\"data row29 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.UniqueRows</td>\n",
-              "      <td id=\"T_686e6_row30_col1\" class=\"data row30 col1\" >Unique Rows</td>\n",
-              "      <td id=\"T_686e6_row30_col2\" class=\"data row30 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
-              "      <td id=\"T_686e6_row30_col3\" class=\"data row30 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row30_col4\" class=\"data row30 col4\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
-              "      <td id=\"T_686e6_row30_col5\" class=\"data row30 col5\" >['tabular_data']</td>\n",
-              "      <td id=\"T_686e6_row30_col6\" class=\"data row30 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
-              "      <td id=\"T_686e6_row31_col1\" class=\"data row31 col1\" >WOE Bin Plots</td>\n",
-              "      <td id=\"T_686e6_row31_col2\" class=\"data row31 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
-              "      <td id=\"T_686e6_row31_col3\" class=\"data row31 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row31_col4\" class=\"data row31 col4\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
-              "      <td id=\"T_686e6_row31_col5\" class=\"data row31 col5\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
-              "      <td id=\"T_686e6_row31_col6\" class=\"data row31 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.WOEBinTable</td>\n",
-              "      <td id=\"T_686e6_row32_col1\" class=\"data row32 col1\" >WOE Bin Table</td>\n",
-              "      <td id=\"T_686e6_row32_col2\" class=\"data row32 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
-              "      <td id=\"T_686e6_row32_col3\" class=\"data row32 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row32_col4\" class=\"data row32 col4\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row32_col5\" class=\"data row32 col5\" >['tabular_data', 'categorical_data']</td>\n",
-              "      <td id=\"T_686e6_row32_col6\" class=\"data row32 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
-              "      <td id=\"T_686e6_row33_col1\" class=\"data row33 col1\" >Features AUC</td>\n",
-              "      <td id=\"T_686e6_row33_col2\" class=\"data row33 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
-              "      <td id=\"T_686e6_row33_col3\" class=\"data row33 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row33_col4\" class=\"data row33 col4\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
-              "      <td id=\"T_686e6_row33_col5\" class=\"data row33 col5\" >['feature_importance', 'AUC', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row33_col6\" class=\"data row33 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
-              "      <td id=\"T_686e6_row34_col1\" class=\"data row34 col1\" >Calibration Curve</td>\n",
-              "      <td id=\"T_686e6_row34_col2\" class=\"data row34 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
-              "      <td id=\"T_686e6_row34_col3\" class=\"data row34 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row34_col4\" class=\"data row34 col4\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_686e6_row34_col5\" class=\"data row34 col5\" >['sklearn', 'model_performance', 'classification']</td>\n",
-              "      <td id=\"T_686e6_row34_col6\" class=\"data row34 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_686e6_row35_col1\" class=\"data row35 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_686e6_row35_col2\" class=\"data row35 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_686e6_row35_col3\" class=\"data row35 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row35_col4\" class=\"data row35 col4\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
-              "      <td id=\"T_686e6_row35_col5\" class=\"data row35 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row35_col6\" class=\"data row35 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
-              "      <td id=\"T_686e6_row36_col1\" class=\"data row36 col1\" >Classifier Threshold Optimization</td>\n",
-              "      <td id=\"T_686e6_row36_col2\" class=\"data row36 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
-              "      <td id=\"T_686e6_row36_col3\" class=\"data row36 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row36_col4\" class=\"data row36 col4\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row36_col5\" class=\"data row36 col5\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
-              "      <td id=\"T_686e6_row36_col6\" class=\"data row36 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_686e6_row37_col1\" class=\"data row37 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_686e6_row37_col2\" class=\"data row37 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_686e6_row37_col3\" class=\"data row37 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row37_col4\" class=\"data row37 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_686e6_row37_col5\" class=\"data row37 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row37_col6\" class=\"data row37 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row38_col0\" class=\"data row38 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_686e6_row38_col1\" class=\"data row38 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_686e6_row38_col2\" class=\"data row38 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
-              "      <td id=\"T_686e6_row38_col3\" class=\"data row38 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row38_col4\" class=\"data row38 col4\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row38_col5\" class=\"data row38 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row38_col6\" class=\"data row38 col6\" >['clustering', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row39_col0\" class=\"data row39 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_686e6_row39_col1\" class=\"data row39 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_686e6_row39_col2\" class=\"data row39 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_686e6_row39_col3\" class=\"data row39 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row39_col4\" class=\"data row39 col4\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
-              "      <td id=\"T_686e6_row39_col5\" class=\"data row39 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row39_col6\" class=\"data row39 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row40_col0\" class=\"data row40 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_686e6_row40_col1\" class=\"data row40 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_686e6_row40_col2\" class=\"data row40 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
-              "      <td id=\"T_686e6_row40_col3\" class=\"data row40 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row40_col4\" class=\"data row40 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_686e6_row40_col5\" class=\"data row40 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row40_col6\" class=\"data row40 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row41_col0\" class=\"data row41 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_686e6_row41_col1\" class=\"data row41 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_686e6_row41_col2\" class=\"data row41 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_686e6_row41_col3\" class=\"data row41 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row41_col4\" class=\"data row41 col4\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_686e6_row41_col5\" class=\"data row41 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row41_col6\" class=\"data row41 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row42_col0\" class=\"data row42 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
-              "      <td id=\"T_686e6_row42_col1\" class=\"data row42 col1\" >Model Parameters</td>\n",
-              "      <td id=\"T_686e6_row42_col2\" class=\"data row42 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
-              "      <td id=\"T_686e6_row42_col3\" class=\"data row42 col3\" >['model']</td>\n",
-              "      <td id=\"T_686e6_row42_col4\" class=\"data row42 col4\" >{'model_params': {'type': '_empty', 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row42_col5\" class=\"data row42 col5\" >['model_training', 'metadata']</td>\n",
-              "      <td id=\"T_686e6_row42_col6\" class=\"data row42 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row43_col0\" class=\"data row43 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_686e6_row43_col1\" class=\"data row43 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_686e6_row43_col2\" class=\"data row43 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_686e6_row43_col3\" class=\"data row43 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_686e6_row43_col4\" class=\"data row43 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row43_col5\" class=\"data row43 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
-              "      <td id=\"T_686e6_row43_col6\" class=\"data row43 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row44_col0\" class=\"data row44 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_686e6_row44_col1\" class=\"data row44 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_686e6_row44_col2\" class=\"data row44 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
-              "      <td id=\"T_686e6_row44_col3\" class=\"data row44 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_686e6_row44_col4\" class=\"data row44 col4\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
-              "      <td id=\"T_686e6_row44_col5\" class=\"data row44 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
-              "      <td id=\"T_686e6_row44_col6\" class=\"data row44 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row45_col0\" class=\"data row45 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_686e6_row45_col1\" class=\"data row45 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_686e6_row45_col2\" class=\"data row45 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_686e6_row45_col3\" class=\"data row45 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row45_col4\" class=\"data row45 col4\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row45_col5\" class=\"data row45 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row45_col6\" class=\"data row45 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row46_col0\" class=\"data row46 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_686e6_row46_col1\" class=\"data row46 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_686e6_row46_col2\" class=\"data row46 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_686e6_row46_col3\" class=\"data row46 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row46_col4\" class=\"data row46 col4\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
-              "      <td id=\"T_686e6_row46_col5\" class=\"data row46 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row46_col6\" class=\"data row46 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row47_col0\" class=\"data row47 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_686e6_row47_col1\" class=\"data row47 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_686e6_row47_col2\" class=\"data row47 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_686e6_row47_col3\" class=\"data row47 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row47_col4\" class=\"data row47 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row47_col5\" class=\"data row47 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row47_col6\" class=\"data row47 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row48_col0\" class=\"data row48 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_686e6_row48_col1\" class=\"data row48 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_686e6_row48_col2\" class=\"data row48 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_686e6_row48_col3\" class=\"data row48 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row48_col4\" class=\"data row48 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row48_col5\" class=\"data row48 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row48_col6\" class=\"data row48 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row49_col0\" class=\"data row49 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
-              "      <td id=\"T_686e6_row49_col1\" class=\"data row49 col1\" >Regression Errors</td>\n",
-              "      <td id=\"T_686e6_row49_col2\" class=\"data row49 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
-              "      <td id=\"T_686e6_row49_col3\" class=\"data row49 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row49_col4\" class=\"data row49 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row49_col5\" class=\"data row49 col5\" >['sklearn', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row49_col6\" class=\"data row49 col6\" >['regression', 'classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row50_col0\" class=\"data row50 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_686e6_row50_col1\" class=\"data row50 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_686e6_row50_col2\" class=\"data row50 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
-              "      <td id=\"T_686e6_row50_col3\" class=\"data row50 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row50_col4\" class=\"data row50 col4\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
-              "      <td id=\"T_686e6_row50_col5\" class=\"data row50 col5\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row50_col6\" class=\"data row50 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row51_col0\" class=\"data row51 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_686e6_row51_col1\" class=\"data row51 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_686e6_row51_col2\" class=\"data row51 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_686e6_row51_col3\" class=\"data row51 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row51_col4\" class=\"data row51 col4\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row51_col5\" class=\"data row51 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row51_col6\" class=\"data row51 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row52_col0\" class=\"data row52 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
-              "      <td id=\"T_686e6_row52_col1\" class=\"data row52 col1\" >Score Probability Alignment</td>\n",
-              "      <td id=\"T_686e6_row52_col2\" class=\"data row52 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
-              "      <td id=\"T_686e6_row52_col3\" class=\"data row52 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row52_col4\" class=\"data row52 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
-              "      <td id=\"T_686e6_row52_col5\" class=\"data row52 col5\" >['visualization', 'credit_risk', 'calibration']</td>\n",
-              "      <td id=\"T_686e6_row52_col6\" class=\"data row52 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row53_col0\" class=\"data row53 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_686e6_row53_col1\" class=\"data row53 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_686e6_row53_col2\" class=\"data row53 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_686e6_row53_col3\" class=\"data row53 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row53_col4\" class=\"data row53 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_686e6_row53_col5\" class=\"data row53 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row53_col6\" class=\"data row53 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row54_col0\" class=\"data row54 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_686e6_row54_col1\" class=\"data row54 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_686e6_row54_col2\" class=\"data row54 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_686e6_row54_col3\" class=\"data row54 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row54_col4\" class=\"data row54 col4\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
-              "      <td id=\"T_686e6_row54_col5\" class=\"data row54 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row54_col6\" class=\"data row54 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row55_col0\" class=\"data row55 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
-              "      <td id=\"T_686e6_row55_col1\" class=\"data row55 col1\" >Cumulative Prediction Probabilities</td>\n",
-              "      <td id=\"T_686e6_row55_col2\" class=\"data row55 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
-              "      <td id=\"T_686e6_row55_col3\" class=\"data row55 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row55_col4\" class=\"data row55 col4\" >{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}</td>\n",
-              "      <td id=\"T_686e6_row55_col5\" class=\"data row55 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_686e6_row55_col6\" class=\"data row55 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row56_col0\" class=\"data row56 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
-              "      <td id=\"T_686e6_row56_col1\" class=\"data row56 col1\" >GINI Table</td>\n",
-              "      <td id=\"T_686e6_row56_col2\" class=\"data row56 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
-              "      <td id=\"T_686e6_row56_col3\" class=\"data row56 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row56_col4\" class=\"data row56 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row56_col5\" class=\"data row56 col5\" >['model_performance']</td>\n",
-              "      <td id=\"T_686e6_row56_col6\" class=\"data row56 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row57_col0\" class=\"data row57 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
-              "      <td id=\"T_686e6_row57_col1\" class=\"data row57 col1\" >Kolmogorov Smirnov</td>\n",
-              "      <td id=\"T_686e6_row57_col2\" class=\"data row57 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
-              "      <td id=\"T_686e6_row57_col3\" class=\"data row57 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row57_col4\" class=\"data row57 col4\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
-              "      <td id=\"T_686e6_row57_col5\" class=\"data row57 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_686e6_row57_col6\" class=\"data row57 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row58_col0\" class=\"data row58 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
-              "      <td id=\"T_686e6_row58_col1\" class=\"data row58 col1\" >Lilliefors</td>\n",
-              "      <td id=\"T_686e6_row58_col2\" class=\"data row58 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
-              "      <td id=\"T_686e6_row58_col3\" class=\"data row58 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row58_col4\" class=\"data row58 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row58_col5\" class=\"data row58 col5\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
-              "      <td id=\"T_686e6_row58_col6\" class=\"data row58 col6\" >['classification', 'regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row59_col0\" class=\"data row59 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
-              "      <td id=\"T_686e6_row59_col1\" class=\"data row59 col1\" >Prediction Probabilities Histogram</td>\n",
-              "      <td id=\"T_686e6_row59_col2\" class=\"data row59 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
-              "      <td id=\"T_686e6_row59_col3\" class=\"data row59 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row59_col4\" class=\"data row59 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
-              "      <td id=\"T_686e6_row59_col5\" class=\"data row59 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_686e6_row59_col6\" class=\"data row59 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row60_col0\" class=\"data row60 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
-              "      <td id=\"T_686e6_row60_col1\" class=\"data row60 col1\" >Scorecard Histogram</td>\n",
-              "      <td id=\"T_686e6_row60_col2\" class=\"data row60 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
-              "      <td id=\"T_686e6_row60_col3\" class=\"data row60 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_686e6_row60_col4\" class=\"data row60 col4\" >{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}</td>\n",
-              "      <td id=\"T_686e6_row60_col5\" class=\"data row60 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
-              "      <td id=\"T_686e6_row60_col6\" class=\"data row60 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row61_col0\" class=\"data row61 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
-              "      <td id=\"T_686e6_row61_col1\" class=\"data row61 col1\" >Calibration Curve Drift</td>\n",
-              "      <td id=\"T_686e6_row61_col2\" class=\"data row61 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row61_col3\" class=\"data row61 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row61_col4\" class=\"data row61 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
-              "      <td id=\"T_686e6_row61_col5\" class=\"data row61 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row61_col6\" class=\"data row61 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row62_col0\" class=\"data row62 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
-              "      <td id=\"T_686e6_row62_col1\" class=\"data row62 col1\" >Class Discrimination Drift</td>\n",
-              "      <td id=\"T_686e6_row62_col2\" class=\"data row62 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row62_col3\" class=\"data row62 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row62_col4\" class=\"data row62 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_686e6_row62_col5\" class=\"data row62 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row62_col6\" class=\"data row62 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row63_col0\" class=\"data row63 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
-              "      <td id=\"T_686e6_row63_col1\" class=\"data row63 col1\" >Class Imbalance Drift</td>\n",
-              "      <td id=\"T_686e6_row63_col2\" class=\"data row63 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row63_col3\" class=\"data row63 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_686e6_row63_col4\" class=\"data row63 col4\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
-              "      <td id=\"T_686e6_row63_col5\" class=\"data row63 col5\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
-              "      <td id=\"T_686e6_row63_col6\" class=\"data row63 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row64_col0\" class=\"data row64 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
-              "      <td id=\"T_686e6_row64_col1\" class=\"data row64 col1\" >Classification Accuracy Drift</td>\n",
-              "      <td id=\"T_686e6_row64_col2\" class=\"data row64 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row64_col3\" class=\"data row64 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row64_col4\" class=\"data row64 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_686e6_row64_col5\" class=\"data row64 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row64_col6\" class=\"data row64 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row65_col0\" class=\"data row65 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
-              "      <td id=\"T_686e6_row65_col1\" class=\"data row65 col1\" >Confusion Matrix Drift</td>\n",
-              "      <td id=\"T_686e6_row65_col2\" class=\"data row65 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row65_col3\" class=\"data row65 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row65_col4\" class=\"data row65 col4\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
-              "      <td id=\"T_686e6_row65_col5\" class=\"data row65 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
-              "      <td id=\"T_686e6_row65_col6\" class=\"data row65 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row66_col0\" class=\"data row66 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
-              "      <td id=\"T_686e6_row66_col1\" class=\"data row66 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
-              "      <td id=\"T_686e6_row66_col2\" class=\"data row66 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row66_col3\" class=\"data row66 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row66_col4\" class=\"data row66 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row66_col5\" class=\"data row66 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_686e6_row66_col6\" class=\"data row66 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row67_col0\" class=\"data row67 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
-              "      <td id=\"T_686e6_row67_col1\" class=\"data row67 col1\" >Prediction Probabilities Histogram Drift</td>\n",
-              "      <td id=\"T_686e6_row67_col2\" class=\"data row67 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row67_col3\" class=\"data row67 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row67_col4\" class=\"data row67 col4\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_686e6_row67_col5\" class=\"data row67 col5\" >['visualization', 'credit_risk']</td>\n",
-              "      <td id=\"T_686e6_row67_col6\" class=\"data row67 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row68_col0\" class=\"data row68 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
-              "      <td id=\"T_686e6_row68_col1\" class=\"data row68 col1\" >ROC Curve Drift</td>\n",
-              "      <td id=\"T_686e6_row68_col2\" class=\"data row68 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_686e6_row68_col3\" class=\"data row68 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row68_col4\" class=\"data row68 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row68_col5\" class=\"data row68 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_686e6_row68_col6\" class=\"data row68 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row69_col0\" class=\"data row69 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
-              "      <td id=\"T_686e6_row69_col1\" class=\"data row69 col1\" >Score Bands Drift</td>\n",
-              "      <td id=\"T_686e6_row69_col2\" class=\"data row69 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
-              "      <td id=\"T_686e6_row69_col3\" class=\"data row69 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_686e6_row69_col4\" class=\"data row69 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_686e6_row69_col5\" class=\"data row69 col5\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
-              "      <td id=\"T_686e6_row69_col6\" class=\"data row69 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row70_col0\" class=\"data row70 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
-              "      <td id=\"T_686e6_row70_col1\" class=\"data row70 col1\" >Scorecard Histogram Drift</td>\n",
-              "      <td id=\"T_686e6_row70_col2\" class=\"data row70 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
-              "      <td id=\"T_686e6_row70_col3\" class=\"data row70 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_686e6_row70_col4\" class=\"data row70 col4\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
-              "      <td id=\"T_686e6_row70_col5\" class=\"data row70 col5\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
-              "      <td id=\"T_686e6_row70_col6\" class=\"data row70 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row71_col0\" class=\"data row71 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
-              "      <td id=\"T_686e6_row71_col1\" class=\"data row71 col1\" >Accuracy</td>\n",
-              "      <td id=\"T_686e6_row71_col2\" class=\"data row71 col2\" >Calculates the accuracy of a model</td>\n",
-              "      <td id=\"T_686e6_row71_col3\" class=\"data row71 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_686e6_row71_col4\" class=\"data row71 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row71_col5\" class=\"data row71 col5\" >['classification']</td>\n",
-              "      <td id=\"T_686e6_row71_col6\" class=\"data row71 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row72_col0\" class=\"data row72 col0\" >validmind.unit_metrics.classification.F1</td>\n",
-              "      <td id=\"T_686e6_row72_col1\" class=\"data row72 col1\" >F1</td>\n",
-              "      <td id=\"T_686e6_row72_col2\" class=\"data row72 col2\" >Calculates the F1 score for a classification model.</td>\n",
-              "      <td id=\"T_686e6_row72_col3\" class=\"data row72 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row72_col4\" class=\"data row72 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row72_col5\" class=\"data row72 col5\" >['classification']</td>\n",
-              "      <td id=\"T_686e6_row72_col6\" class=\"data row72 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row73_col0\" class=\"data row73 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
-              "      <td id=\"T_686e6_row73_col1\" class=\"data row73 col1\" >Precision</td>\n",
-              "      <td id=\"T_686e6_row73_col2\" class=\"data row73 col2\" >Calculates the precision for a classification model.</td>\n",
-              "      <td id=\"T_686e6_row73_col3\" class=\"data row73 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row73_col4\" class=\"data row73 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row73_col5\" class=\"data row73 col5\" >['classification']</td>\n",
-              "      <td id=\"T_686e6_row73_col6\" class=\"data row73 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row74_col0\" class=\"data row74 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
-              "      <td id=\"T_686e6_row74_col1\" class=\"data row74 col1\" >ROC AUC</td>\n",
-              "      <td id=\"T_686e6_row74_col2\" class=\"data row74 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
-              "      <td id=\"T_686e6_row74_col3\" class=\"data row74 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row74_col4\" class=\"data row74 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row74_col5\" class=\"data row74 col5\" >['classification']</td>\n",
-              "      <td id=\"T_686e6_row74_col6\" class=\"data row74 col6\" >['classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_686e6_row75_col0\" class=\"data row75 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
-              "      <td id=\"T_686e6_row75_col1\" class=\"data row75 col1\" >Recall</td>\n",
-              "      <td id=\"T_686e6_row75_col2\" class=\"data row75 col2\" >Calculates the recall for a classification model.</td>\n",
-              "      <td id=\"T_686e6_row75_col3\" class=\"data row75 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_686e6_row75_col4\" class=\"data row75 col4\" >{}</td>\n",
-              "      <td id=\"T_686e6_row75_col5\" class=\"data row75 col5\" >['classification']</td>\n",
-              "      <td id=\"T_686e6_row75_col6\" class=\"data row75 col6\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row0_col0\" class=\"data row0 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
+              "      <td id=\"T_56dd5_row0_col1\" class=\"data row0 col1\" >Bivariate Scatter Plots</td>\n",
+              "      <td id=\"T_56dd5_row0_col2\" class=\"data row0 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...</td>\n",
+              "      <td id=\"T_56dd5_row0_col3\" class=\"data row0 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row0_col4\" class=\"data row0 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row0_col5\" class=\"data row0 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row0_col6\" class=\"data row0 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row0_col7\" class=\"data row0 col7\" >['tabular_data', 'numerical_data', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row0_col8\" class=\"data row0 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row1_col0\" class=\"data row1 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
+              "      <td id=\"T_56dd5_row1_col1\" class=\"data row1 col1\" >Chi Squared Features Table</td>\n",
+              "      <td id=\"T_56dd5_row1_col2\" class=\"data row1 col2\" >Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....</td>\n",
+              "      <td id=\"T_56dd5_row1_col3\" class=\"data row1 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row1_col4\" class=\"data row1 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row1_col5\" class=\"data row1 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row1_col6\" class=\"data row1 col6\" >{'p_threshold': {'type': '_empty', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_56dd5_row1_col7\" class=\"data row1 col7\" >['tabular_data', 'categorical_data', 'statistical_test']</td>\n",
+              "      <td id=\"T_56dd5_row1_col8\" class=\"data row1 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row2_col0\" class=\"data row2 col0\" >validmind.data_validation.ClassImbalance</td>\n",
+              "      <td id=\"T_56dd5_row2_col1\" class=\"data row2 col1\" >Class Imbalance</td>\n",
+              "      <td id=\"T_56dd5_row2_col2\" class=\"data row2 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
+              "      <td id=\"T_56dd5_row2_col3\" class=\"data row2 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row2_col4\" class=\"data row2 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row2_col5\" class=\"data row2 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row2_col6\" class=\"data row2 col6\" >{'min_percent_threshold': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_56dd5_row2_col7\" class=\"data row2 col7\" >['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']</td>\n",
+              "      <td id=\"T_56dd5_row2_col8\" class=\"data row2 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row3_col0\" class=\"data row3 col0\" >validmind.data_validation.DatasetDescription</td>\n",
+              "      <td id=\"T_56dd5_row3_col1\" class=\"data row3 col1\" >Dataset Description</td>\n",
+              "      <td id=\"T_56dd5_row3_col2\" class=\"data row3 col2\" >Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....</td>\n",
+              "      <td id=\"T_56dd5_row3_col3\" class=\"data row3 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row3_col4\" class=\"data row3 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row3_col5\" class=\"data row3 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row3_col6\" class=\"data row3 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row3_col7\" class=\"data row3 col7\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
+              "      <td id=\"T_56dd5_row3_col8\" class=\"data row3 col8\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row4_col0\" class=\"data row4 col0\" >validmind.data_validation.DatasetSplit</td>\n",
+              "      <td id=\"T_56dd5_row4_col1\" class=\"data row4 col1\" >Dataset Split</td>\n",
+              "      <td id=\"T_56dd5_row4_col2\" class=\"data row4 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
+              "      <td id=\"T_56dd5_row4_col3\" class=\"data row4 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row4_col4\" class=\"data row4 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row4_col5\" class=\"data row4 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_56dd5_row4_col6\" class=\"data row4 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row4_col7\" class=\"data row4 col7\" >['tabular_data', 'time_series_data', 'text_data']</td>\n",
+              "      <td id=\"T_56dd5_row4_col8\" class=\"data row4 col8\" >['classification', 'regression', 'text_classification', 'text_summarization']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row5_col0\" class=\"data row5 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
+              "      <td id=\"T_56dd5_row5_col1\" class=\"data row5 col1\" >Descriptive Statistics</td>\n",
+              "      <td id=\"T_56dd5_row5_col2\" class=\"data row5 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
+              "      <td id=\"T_56dd5_row5_col3\" class=\"data row5 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row5_col4\" class=\"data row5 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row5_col5\" class=\"data row5 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row5_col6\" class=\"data row5 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row5_col7\" class=\"data row5 col7\" >['tabular_data', 'time_series_data', 'data_quality']</td>\n",
+              "      <td id=\"T_56dd5_row5_col8\" class=\"data row5 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row6_col0\" class=\"data row6 col0\" >validmind.data_validation.Duplicates</td>\n",
+              "      <td id=\"T_56dd5_row6_col1\" class=\"data row6 col1\" >Duplicates</td>\n",
+              "      <td id=\"T_56dd5_row6_col2\" class=\"data row6 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
+              "      <td id=\"T_56dd5_row6_col3\" class=\"data row6 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row6_col4\" class=\"data row6 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row6_col5\" class=\"data row6 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row6_col6\" class=\"data row6 col6\" >{'min_threshold': {'type': '_empty', 'default': 1}}</td>\n",
+              "      <td id=\"T_56dd5_row6_col7\" class=\"data row6 col7\" >['tabular_data', 'data_quality', 'text_data']</td>\n",
+              "      <td id=\"T_56dd5_row6_col8\" class=\"data row6 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row7_col0\" class=\"data row7 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
+              "      <td id=\"T_56dd5_row7_col1\" class=\"data row7 col1\" >Feature Target Correlation Plot</td>\n",
+              "      <td id=\"T_56dd5_row7_col2\" class=\"data row7 col2\" >Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...</td>\n",
+              "      <td id=\"T_56dd5_row7_col3\" class=\"data row7 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row7_col4\" class=\"data row7 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row7_col5\" class=\"data row7 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row7_col6\" class=\"data row7 col6\" >{'fig_height': {'type': '_empty', 'default': 600}}</td>\n",
+              "      <td id=\"T_56dd5_row7_col7\" class=\"data row7 col7\" >['tabular_data', 'visualization', 'correlation']</td>\n",
+              "      <td id=\"T_56dd5_row7_col8\" class=\"data row7 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row8_col0\" class=\"data row8 col0\" >validmind.data_validation.HighCardinality</td>\n",
+              "      <td id=\"T_56dd5_row8_col1\" class=\"data row8 col1\" >High Cardinality</td>\n",
+              "      <td id=\"T_56dd5_row8_col2\" class=\"data row8 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
+              "      <td id=\"T_56dd5_row8_col3\" class=\"data row8 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row8_col4\" class=\"data row8 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row8_col5\" class=\"data row8 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row8_col6\" class=\"data row8 col6\" >{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}</td>\n",
+              "      <td id=\"T_56dd5_row8_col7\" class=\"data row8 col7\" >['tabular_data', 'data_quality', 'categorical_data']</td>\n",
+              "      <td id=\"T_56dd5_row8_col8\" class=\"data row8 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row9_col0\" class=\"data row9 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
+              "      <td id=\"T_56dd5_row9_col1\" class=\"data row9 col1\" >High Pearson Correlation</td>\n",
+              "      <td id=\"T_56dd5_row9_col2\" class=\"data row9 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
+              "      <td id=\"T_56dd5_row9_col3\" class=\"data row9 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row9_col4\" class=\"data row9 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row9_col5\" class=\"data row9 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row9_col6\" class=\"data row9 col6\" >{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row9_col7\" class=\"data row9 col7\" >['tabular_data', 'data_quality', 'correlation']</td>\n",
+              "      <td id=\"T_56dd5_row9_col8\" class=\"data row9 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row10_col0\" class=\"data row10 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
+              "      <td id=\"T_56dd5_row10_col1\" class=\"data row10 col1\" >IQR Outliers Bar Plot</td>\n",
+              "      <td id=\"T_56dd5_row10_col2\" class=\"data row10 col2\" >Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....</td>\n",
+              "      <td id=\"T_56dd5_row10_col3\" class=\"data row10 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row10_col4\" class=\"data row10 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row10_col5\" class=\"data row10 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row10_col6\" class=\"data row10 col6\" >{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}</td>\n",
+              "      <td id=\"T_56dd5_row10_col7\" class=\"data row10 col7\" >['tabular_data', 'visualization', 'numerical_data']</td>\n",
+              "      <td id=\"T_56dd5_row10_col8\" class=\"data row10 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row11_col0\" class=\"data row11 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
+              "      <td id=\"T_56dd5_row11_col1\" class=\"data row11 col1\" >IQR Outliers Table</td>\n",
+              "      <td id=\"T_56dd5_row11_col2\" class=\"data row11 col2\" >Determines and summarizes outliers in numerical features using the Interquartile Range method....</td>\n",
+              "      <td id=\"T_56dd5_row11_col3\" class=\"data row11 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row11_col4\" class=\"data row11 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row11_col5\" class=\"data row11 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row11_col6\" class=\"data row11 col6\" >{'threshold': {'type': 'float', 'default': 1.5}}</td>\n",
+              "      <td id=\"T_56dd5_row11_col7\" class=\"data row11 col7\" >['tabular_data', 'numerical_data']</td>\n",
+              "      <td id=\"T_56dd5_row11_col8\" class=\"data row11 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row12_col0\" class=\"data row12 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
+              "      <td id=\"T_56dd5_row12_col1\" class=\"data row12 col1\" >Isolation Forest Outliers</td>\n",
+              "      <td id=\"T_56dd5_row12_col2\" class=\"data row12 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
+              "      <td id=\"T_56dd5_row12_col3\" class=\"data row12 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row12_col4\" class=\"data row12 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row12_col5\" class=\"data row12 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row12_col6\" class=\"data row12 col6\" >{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row12_col7\" class=\"data row12 col7\" >['tabular_data', 'anomaly_detection']</td>\n",
+              "      <td id=\"T_56dd5_row12_col8\" class=\"data row12 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row13_col0\" class=\"data row13 col0\" >validmind.data_validation.JarqueBera</td>\n",
+              "      <td id=\"T_56dd5_row13_col1\" class=\"data row13 col1\" >Jarque Bera</td>\n",
+              "      <td id=\"T_56dd5_row13_col2\" class=\"data row13 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
+              "      <td id=\"T_56dd5_row13_col3\" class=\"data row13 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row13_col4\" class=\"data row13 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row13_col5\" class=\"data row13 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row13_col6\" class=\"data row13 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row13_col7\" class=\"data row13 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_56dd5_row13_col8\" class=\"data row13 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row14_col0\" class=\"data row14 col0\" >validmind.data_validation.MissingValues</td>\n",
+              "      <td id=\"T_56dd5_row14_col1\" class=\"data row14 col1\" >Missing Values</td>\n",
+              "      <td id=\"T_56dd5_row14_col2\" class=\"data row14 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
+              "      <td id=\"T_56dd5_row14_col3\" class=\"data row14 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row14_col4\" class=\"data row14 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row14_col5\" class=\"data row14 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row14_col6\" class=\"data row14 col6\" >{'min_threshold': {'type': 'int', 'default': 1}}</td>\n",
+              "      <td id=\"T_56dd5_row14_col7\" class=\"data row14 col7\" >['tabular_data', 'data_quality']</td>\n",
+              "      <td id=\"T_56dd5_row14_col8\" class=\"data row14 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row15_col0\" class=\"data row15 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
+              "      <td id=\"T_56dd5_row15_col1\" class=\"data row15 col1\" >Missing Values Bar Plot</td>\n",
+              "      <td id=\"T_56dd5_row15_col2\" class=\"data row15 col2\" >Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...</td>\n",
+              "      <td id=\"T_56dd5_row15_col3\" class=\"data row15 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row15_col4\" class=\"data row15 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row15_col5\" class=\"data row15 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row15_col6\" class=\"data row15 col6\" >{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}</td>\n",
+              "      <td id=\"T_56dd5_row15_col7\" class=\"data row15 col7\" >['tabular_data', 'data_quality', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row15_col8\" class=\"data row15 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row16_col0\" class=\"data row16 col0\" >validmind.data_validation.MutualInformation</td>\n",
+              "      <td id=\"T_56dd5_row16_col1\" class=\"data row16 col1\" >Mutual Information</td>\n",
+              "      <td id=\"T_56dd5_row16_col2\" class=\"data row16 col2\" >Calculates mutual information scores between features and target variable to evaluate feature relevance....</td>\n",
+              "      <td id=\"T_56dd5_row16_col3\" class=\"data row16 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row16_col4\" class=\"data row16 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row16_col5\" class=\"data row16 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row16_col6\" class=\"data row16 col6\" >{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}</td>\n",
+              "      <td id=\"T_56dd5_row16_col7\" class=\"data row16 col7\" >['feature_selection', 'data_analysis']</td>\n",
+              "      <td id=\"T_56dd5_row16_col8\" class=\"data row16 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row17_col0\" class=\"data row17 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
+              "      <td id=\"T_56dd5_row17_col1\" class=\"data row17 col1\" >Pearson Correlation Matrix</td>\n",
+              "      <td id=\"T_56dd5_row17_col2\" class=\"data row17 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
+              "      <td id=\"T_56dd5_row17_col3\" class=\"data row17 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row17_col4\" class=\"data row17 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row17_col5\" class=\"data row17 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row17_col6\" class=\"data row17 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row17_col7\" class=\"data row17 col7\" >['tabular_data', 'numerical_data', 'correlation']</td>\n",
+              "      <td id=\"T_56dd5_row17_col8\" class=\"data row17 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row18_col0\" class=\"data row18 col0\" >validmind.data_validation.ProtectedClassesDescription</td>\n",
+              "      <td id=\"T_56dd5_row18_col1\" class=\"data row18 col1\" >Protected Classes Description</td>\n",
+              "      <td id=\"T_56dd5_row18_col2\" class=\"data row18 col2\" >Visualizes the distribution of protected classes in the dataset relative to the target variable...</td>\n",
+              "      <td id=\"T_56dd5_row18_col3\" class=\"data row18 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row18_col4\" class=\"data row18 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row18_col5\" class=\"data row18 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row18_col6\" class=\"data row18 col6\" >{'protected_classes': {'type': '_empty', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row18_col7\" class=\"data row18 col7\" >['bias_and_fairness', 'descriptive_statistics']</td>\n",
+              "      <td id=\"T_56dd5_row18_col8\" class=\"data row18 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row19_col0\" class=\"data row19 col0\" >validmind.data_validation.RunsTest</td>\n",
+              "      <td id=\"T_56dd5_row19_col1\" class=\"data row19 col1\" >Runs Test</td>\n",
+              "      <td id=\"T_56dd5_row19_col2\" class=\"data row19 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
+              "      <td id=\"T_56dd5_row19_col3\" class=\"data row19 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row19_col4\" class=\"data row19 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row19_col5\" class=\"data row19 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row19_col6\" class=\"data row19 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row19_col7\" class=\"data row19 col7\" >['tabular_data', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_56dd5_row19_col8\" class=\"data row19 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row20_col0\" class=\"data row20 col0\" >validmind.data_validation.ScatterPlot</td>\n",
+              "      <td id=\"T_56dd5_row20_col1\" class=\"data row20 col1\" >Scatter Plot</td>\n",
+              "      <td id=\"T_56dd5_row20_col2\" class=\"data row20 col2\" >Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....</td>\n",
+              "      <td id=\"T_56dd5_row20_col3\" class=\"data row20 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row20_col4\" class=\"data row20 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row20_col5\" class=\"data row20 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row20_col6\" class=\"data row20 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row20_col7\" class=\"data row20 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row20_col8\" class=\"data row20 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row21_col0\" class=\"data row21 col0\" >validmind.data_validation.ScoreBandDefaultRates</td>\n",
+              "      <td id=\"T_56dd5_row21_col1\" class=\"data row21 col1\" >Score Band Default Rates</td>\n",
+              "      <td id=\"T_56dd5_row21_col2\" class=\"data row21 col2\" >Analyzes default rates and population distribution across credit score bands....</td>\n",
+              "      <td id=\"T_56dd5_row21_col3\" class=\"data row21 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row21_col4\" class=\"data row21 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row21_col5\" class=\"data row21 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row21_col6\" class=\"data row21 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row21_col7\" class=\"data row21 col7\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
+              "      <td id=\"T_56dd5_row21_col8\" class=\"data row21 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row22_col0\" class=\"data row22 col0\" >validmind.data_validation.ShapiroWilk</td>\n",
+              "      <td id=\"T_56dd5_row22_col1\" class=\"data row22 col1\" >Shapiro Wilk</td>\n",
+              "      <td id=\"T_56dd5_row22_col2\" class=\"data row22 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
+              "      <td id=\"T_56dd5_row22_col3\" class=\"data row22 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row22_col4\" class=\"data row22 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row22_col5\" class=\"data row22 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row22_col6\" class=\"data row22 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row22_col7\" class=\"data row22 col7\" >['tabular_data', 'data_distribution', 'statistical_test']</td>\n",
+              "      <td id=\"T_56dd5_row22_col8\" class=\"data row22 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row23_col0\" class=\"data row23 col0\" >validmind.data_validation.Skewness</td>\n",
+              "      <td id=\"T_56dd5_row23_col1\" class=\"data row23 col1\" >Skewness</td>\n",
+              "      <td id=\"T_56dd5_row23_col2\" class=\"data row23 col2\" >Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...</td>\n",
+              "      <td id=\"T_56dd5_row23_col3\" class=\"data row23 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row23_col4\" class=\"data row23 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row23_col5\" class=\"data row23 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row23_col6\" class=\"data row23 col6\" >{'max_threshold': {'type': '_empty', 'default': 1}}</td>\n",
+              "      <td id=\"T_56dd5_row23_col7\" class=\"data row23 col7\" >['data_quality', 'tabular_data']</td>\n",
+              "      <td id=\"T_56dd5_row23_col8\" class=\"data row23 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row24_col0\" class=\"data row24 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
+              "      <td id=\"T_56dd5_row24_col1\" class=\"data row24 col1\" >Tabular Categorical Bar Plots</td>\n",
+              "      <td id=\"T_56dd5_row24_col2\" class=\"data row24 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....</td>\n",
+              "      <td id=\"T_56dd5_row24_col3\" class=\"data row24 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row24_col4\" class=\"data row24 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row24_col5\" class=\"data row24 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row24_col6\" class=\"data row24 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row24_col7\" class=\"data row24 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row24_col8\" class=\"data row24 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row25_col0\" class=\"data row25 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
+              "      <td id=\"T_56dd5_row25_col1\" class=\"data row25 col1\" >Tabular Date Time Histograms</td>\n",
+              "      <td id=\"T_56dd5_row25_col2\" class=\"data row25 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...</td>\n",
+              "      <td id=\"T_56dd5_row25_col3\" class=\"data row25 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row25_col4\" class=\"data row25 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row25_col5\" class=\"data row25 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row25_col6\" class=\"data row25 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row25_col7\" class=\"data row25 col7\" >['time_series_data', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row25_col8\" class=\"data row25 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row26_col0\" class=\"data row26 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
+              "      <td id=\"T_56dd5_row26_col1\" class=\"data row26 col1\" >Tabular Description Tables</td>\n",
+              "      <td id=\"T_56dd5_row26_col2\" class=\"data row26 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
+              "      <td id=\"T_56dd5_row26_col3\" class=\"data row26 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row26_col4\" class=\"data row26 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row26_col5\" class=\"data row26 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row26_col6\" class=\"data row26 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row26_col7\" class=\"data row26 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_56dd5_row26_col8\" class=\"data row26 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
+              "      <td id=\"T_56dd5_row27_col1\" class=\"data row27 col1\" >Tabular Numerical Histograms</td>\n",
+              "      <td id=\"T_56dd5_row27_col2\" class=\"data row27 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
+              "      <td id=\"T_56dd5_row27_col3\" class=\"data row27 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row27_col4\" class=\"data row27 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row27_col5\" class=\"data row27 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row27_col6\" class=\"data row27 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row27_col7\" class=\"data row27 col7\" >['tabular_data', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row27_col8\" class=\"data row27 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
+              "      <td id=\"T_56dd5_row28_col1\" class=\"data row28 col1\" >Target Rate Bar Plots</td>\n",
+              "      <td id=\"T_56dd5_row28_col2\" class=\"data row28 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
+              "      <td id=\"T_56dd5_row28_col3\" class=\"data row28 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row28_col4\" class=\"data row28 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row28_col5\" class=\"data row28 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row28_col6\" class=\"data row28 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row28_col7\" class=\"data row28 col7\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
+              "      <td id=\"T_56dd5_row28_col8\" class=\"data row28 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
+              "      <td id=\"T_56dd5_row29_col1\" class=\"data row29 col1\" >Too Many Zero Values</td>\n",
+              "      <td id=\"T_56dd5_row29_col2\" class=\"data row29 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
+              "      <td id=\"T_56dd5_row29_col3\" class=\"data row29 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row29_col4\" class=\"data row29 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row29_col5\" class=\"data row29 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row29_col6\" class=\"data row29 col6\" >{'max_percent_threshold': {'type': 'float', 'default': 0.03}}</td>\n",
+              "      <td id=\"T_56dd5_row29_col7\" class=\"data row29 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_56dd5_row29_col8\" class=\"data row29 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.UniqueRows</td>\n",
+              "      <td id=\"T_56dd5_row30_col1\" class=\"data row30 col1\" >Unique Rows</td>\n",
+              "      <td id=\"T_56dd5_row30_col2\" class=\"data row30 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
+              "      <td id=\"T_56dd5_row30_col3\" class=\"data row30 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row30_col4\" class=\"data row30 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row30_col5\" class=\"data row30 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row30_col6\" class=\"data row30 col6\" >{'min_percent_threshold': {'type': 'float', 'default': 1}}</td>\n",
+              "      <td id=\"T_56dd5_row30_col7\" class=\"data row30 col7\" >['tabular_data']</td>\n",
+              "      <td id=\"T_56dd5_row30_col8\" class=\"data row30 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
+              "      <td id=\"T_56dd5_row31_col1\" class=\"data row31 col1\" >WOE Bin Plots</td>\n",
+              "      <td id=\"T_56dd5_row31_col2\" class=\"data row31 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
+              "      <td id=\"T_56dd5_row31_col3\" class=\"data row31 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row31_col4\" class=\"data row31 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row31_col5\" class=\"data row31 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row31_col6\" class=\"data row31 col6\" >{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}</td>\n",
+              "      <td id=\"T_56dd5_row31_col7\" class=\"data row31 col7\" >['tabular_data', 'visualization', 'categorical_data']</td>\n",
+              "      <td id=\"T_56dd5_row31_col8\" class=\"data row31 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.WOEBinTable</td>\n",
+              "      <td id=\"T_56dd5_row32_col1\" class=\"data row32 col1\" >WOE Bin Table</td>\n",
+              "      <td id=\"T_56dd5_row32_col2\" class=\"data row32 col2\" >Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...</td>\n",
+              "      <td id=\"T_56dd5_row32_col3\" class=\"data row32 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row32_col4\" class=\"data row32 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row32_col5\" class=\"data row32 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row32_col6\" class=\"data row32 col6\" >{'breaks_adj': {'type': 'list', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row32_col7\" class=\"data row32 col7\" >['tabular_data', 'categorical_data']</td>\n",
+              "      <td id=\"T_56dd5_row32_col8\" class=\"data row32 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
+              "      <td id=\"T_56dd5_row33_col1\" class=\"data row33 col1\" >Features AUC</td>\n",
+              "      <td id=\"T_56dd5_row33_col2\" class=\"data row33 col2\" >Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...</td>\n",
+              "      <td id=\"T_56dd5_row33_col3\" class=\"data row33 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row33_col4\" class=\"data row33 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row33_col5\" class=\"data row33 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row33_col6\" class=\"data row33 col6\" >{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}</td>\n",
+              "      <td id=\"T_56dd5_row33_col7\" class=\"data row33 col7\" >['feature_importance', 'AUC', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row33_col8\" class=\"data row33 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.CalibrationCurve</td>\n",
+              "      <td id=\"T_56dd5_row34_col1\" class=\"data row34 col1\" >Calibration Curve</td>\n",
+              "      <td id=\"T_56dd5_row34_col2\" class=\"data row34 col2\" >Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...</td>\n",
+              "      <td id=\"T_56dd5_row34_col3\" class=\"data row34 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row34_col4\" class=\"data row34 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row34_col5\" class=\"data row34 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row34_col6\" class=\"data row34 col6\" >{'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_56dd5_row34_col7\" class=\"data row34 col7\" >['sklearn', 'model_performance', 'classification']</td>\n",
+              "      <td id=\"T_56dd5_row34_col8\" class=\"data row34 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
+              "      <td id=\"T_56dd5_row35_col1\" class=\"data row35 col1\" >Classifier Performance</td>\n",
+              "      <td id=\"T_56dd5_row35_col2\" class=\"data row35 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
+              "      <td id=\"T_56dd5_row35_col3\" class=\"data row35 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row35_col4\" class=\"data row35 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row35_col5\" class=\"data row35 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row35_col6\" class=\"data row35 col6\" >{'average': {'type': 'str', 'default': 'macro'}}</td>\n",
+              "      <td id=\"T_56dd5_row35_col7\" class=\"data row35 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row35_col8\" class=\"data row35 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.sklearn.ClassifierThresholdOptimization</td>\n",
+              "      <td id=\"T_56dd5_row36_col1\" class=\"data row36 col1\" >Classifier Threshold Optimization</td>\n",
+              "      <td id=\"T_56dd5_row36_col2\" class=\"data row36 col2\" >Analyzes and visualizes different threshold optimization methods for binary classification models....</td>\n",
+              "      <td id=\"T_56dd5_row36_col3\" class=\"data row36 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row36_col4\" class=\"data row36 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row36_col5\" class=\"data row36 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row36_col6\" class=\"data row36 col6\" >{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row36_col7\" class=\"data row36 col7\" >['model_validation', 'threshold_optimization', 'classification_metrics']</td>\n",
+              "      <td id=\"T_56dd5_row36_col8\" class=\"data row36 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
+              "      <td id=\"T_56dd5_row37_col1\" class=\"data row37 col1\" >Confusion Matrix</td>\n",
+              "      <td id=\"T_56dd5_row37_col2\" class=\"data row37 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
+              "      <td id=\"T_56dd5_row37_col3\" class=\"data row37 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row37_col4\" class=\"data row37 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row37_col5\" class=\"data row37 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row37_col6\" class=\"data row37 col6\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_56dd5_row37_col7\" class=\"data row37 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row37_col8\" class=\"data row37 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row38_col0\" class=\"data row38 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
+              "      <td id=\"T_56dd5_row38_col1\" class=\"data row38 col1\" >Hyper Parameters Tuning</td>\n",
+              "      <td id=\"T_56dd5_row38_col2\" class=\"data row38 col2\" >Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...</td>\n",
+              "      <td id=\"T_56dd5_row38_col3\" class=\"data row38 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row38_col4\" class=\"data row38 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row38_col5\" class=\"data row38 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row38_col6\" class=\"data row38 col6\" >{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row38_col7\" class=\"data row38 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row38_col8\" class=\"data row38 col8\" >['clustering', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row39_col0\" class=\"data row39 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
+              "      <td id=\"T_56dd5_row39_col1\" class=\"data row39 col1\" >Minimum Accuracy</td>\n",
+              "      <td id=\"T_56dd5_row39_col2\" class=\"data row39 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_56dd5_row39_col3\" class=\"data row39 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row39_col4\" class=\"data row39 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row39_col5\" class=\"data row39 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row39_col6\" class=\"data row39 col6\" >{'min_threshold': {'type': 'float', 'default': 0.7}}</td>\n",
+              "      <td id=\"T_56dd5_row39_col7\" class=\"data row39 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row39_col8\" class=\"data row39 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row40_col0\" class=\"data row40 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
+              "      <td id=\"T_56dd5_row40_col1\" class=\"data row40 col1\" >Minimum F1 Score</td>\n",
+              "      <td id=\"T_56dd5_row40_col2\" class=\"data row40 col2\" >Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...</td>\n",
+              "      <td id=\"T_56dd5_row40_col3\" class=\"data row40 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row40_col4\" class=\"data row40 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row40_col5\" class=\"data row40 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row40_col6\" class=\"data row40 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_56dd5_row40_col7\" class=\"data row40 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row40_col8\" class=\"data row40 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row41_col0\" class=\"data row41 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
+              "      <td id=\"T_56dd5_row41_col1\" class=\"data row41 col1\" >Minimum ROCAUC Score</td>\n",
+              "      <td id=\"T_56dd5_row41_col2\" class=\"data row41 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
+              "      <td id=\"T_56dd5_row41_col3\" class=\"data row41 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row41_col4\" class=\"data row41 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row41_col5\" class=\"data row41 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row41_col6\" class=\"data row41 col6\" >{'min_threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_56dd5_row41_col7\" class=\"data row41 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row41_col8\" class=\"data row41 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row42_col0\" class=\"data row42 col0\" >validmind.model_validation.sklearn.ModelParameters</td>\n",
+              "      <td id=\"T_56dd5_row42_col1\" class=\"data row42 col1\" >Model Parameters</td>\n",
+              "      <td id=\"T_56dd5_row42_col2\" class=\"data row42 col2\" >Extracts and displays model parameters in a structured format for transparency and reproducibility....</td>\n",
+              "      <td id=\"T_56dd5_row42_col3\" class=\"data row42 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row42_col4\" class=\"data row42 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row42_col5\" class=\"data row42 col5\" >['model']</td>\n",
+              "      <td id=\"T_56dd5_row42_col6\" class=\"data row42 col6\" >{'model_params': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row42_col7\" class=\"data row42 col7\" >['model_training', 'metadata']</td>\n",
+              "      <td id=\"T_56dd5_row42_col8\" class=\"data row42 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row43_col0\" class=\"data row43 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
+              "      <td id=\"T_56dd5_row43_col1\" class=\"data row43 col1\" >Models Performance Comparison</td>\n",
+              "      <td id=\"T_56dd5_row43_col2\" class=\"data row43 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
+              "      <td id=\"T_56dd5_row43_col3\" class=\"data row43 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row43_col4\" class=\"data row43 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row43_col5\" class=\"data row43 col5\" >['dataset', 'models']</td>\n",
+              "      <td id=\"T_56dd5_row43_col6\" class=\"data row43 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row43_col7\" class=\"data row43 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']</td>\n",
+              "      <td id=\"T_56dd5_row43_col8\" class=\"data row43 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row44_col0\" class=\"data row44 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
+              "      <td id=\"T_56dd5_row44_col1\" class=\"data row44 col1\" >Overfit Diagnosis</td>\n",
+              "      <td id=\"T_56dd5_row44_col2\" class=\"data row44 col2\" >Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...</td>\n",
+              "      <td id=\"T_56dd5_row44_col3\" class=\"data row44 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row44_col4\" class=\"data row44 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row44_col5\" class=\"data row44 col5\" >['model', 'datasets']</td>\n",
+              "      <td id=\"T_56dd5_row44_col6\" class=\"data row44 col6\" >{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}</td>\n",
+              "      <td id=\"T_56dd5_row44_col7\" class=\"data row44 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']</td>\n",
+              "      <td id=\"T_56dd5_row44_col8\" class=\"data row44 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row45_col0\" class=\"data row45 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
+              "      <td id=\"T_56dd5_row45_col1\" class=\"data row45 col1\" >Permutation Feature Importance</td>\n",
+              "      <td id=\"T_56dd5_row45_col2\" class=\"data row45 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
+              "      <td id=\"T_56dd5_row45_col3\" class=\"data row45 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row45_col4\" class=\"data row45 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row45_col5\" class=\"data row45 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row45_col6\" class=\"data row45 col6\" >{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row45_col7\" class=\"data row45 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row45_col8\" class=\"data row45 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row46_col0\" class=\"data row46 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
+              "      <td id=\"T_56dd5_row46_col1\" class=\"data row46 col1\" >Population Stability Index</td>\n",
+              "      <td id=\"T_56dd5_row46_col2\" class=\"data row46 col2\" >Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
+              "      <td id=\"T_56dd5_row46_col3\" class=\"data row46 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row46_col4\" class=\"data row46 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row46_col5\" class=\"data row46 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row46_col6\" class=\"data row46 col6\" >{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}</td>\n",
+              "      <td id=\"T_56dd5_row46_col7\" class=\"data row46 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row46_col8\" class=\"data row46 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row47_col0\" class=\"data row47 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
+              "      <td id=\"T_56dd5_row47_col1\" class=\"data row47 col1\" >Precision Recall Curve</td>\n",
+              "      <td id=\"T_56dd5_row47_col2\" class=\"data row47 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
+              "      <td id=\"T_56dd5_row47_col3\" class=\"data row47 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row47_col4\" class=\"data row47 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row47_col5\" class=\"data row47 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row47_col6\" class=\"data row47 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row47_col7\" class=\"data row47 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row47_col8\" class=\"data row47 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row48_col0\" class=\"data row48 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
+              "      <td id=\"T_56dd5_row48_col1\" class=\"data row48 col1\" >ROC Curve</td>\n",
+              "      <td id=\"T_56dd5_row48_col2\" class=\"data row48 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
+              "      <td id=\"T_56dd5_row48_col3\" class=\"data row48 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row48_col4\" class=\"data row48 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row48_col5\" class=\"data row48 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row48_col6\" class=\"data row48 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row48_col7\" class=\"data row48 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row48_col8\" class=\"data row48 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row49_col0\" class=\"data row49 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
+              "      <td id=\"T_56dd5_row49_col1\" class=\"data row49 col1\" >Regression Errors</td>\n",
+              "      <td id=\"T_56dd5_row49_col2\" class=\"data row49 col2\" >Assesses the performance and error distribution of a regression model using various error metrics....</td>\n",
+              "      <td id=\"T_56dd5_row49_col3\" class=\"data row49 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row49_col4\" class=\"data row49 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row49_col5\" class=\"data row49 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row49_col6\" class=\"data row49 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row49_col7\" class=\"data row49 col7\" >['sklearn', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row49_col8\" class=\"data row49 col8\" >['regression', 'classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row50_col0\" class=\"data row50 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
+              "      <td id=\"T_56dd5_row50_col1\" class=\"data row50 col1\" >Robustness Diagnosis</td>\n",
+              "      <td id=\"T_56dd5_row50_col2\" class=\"data row50 col2\" >Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....</td>\n",
+              "      <td id=\"T_56dd5_row50_col3\" class=\"data row50 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row50_col4\" class=\"data row50 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row50_col5\" class=\"data row50 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row50_col6\" class=\"data row50 col6\" >{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}</td>\n",
+              "      <td id=\"T_56dd5_row50_col7\" class=\"data row50 col7\" >['sklearn', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row50_col8\" class=\"data row50 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row51_col0\" class=\"data row51 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
+              "      <td id=\"T_56dd5_row51_col1\" class=\"data row51 col1\" >SHAP Global Importance</td>\n",
+              "      <td id=\"T_56dd5_row51_col2\" class=\"data row51 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
+              "      <td id=\"T_56dd5_row51_col3\" class=\"data row51 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row51_col4\" class=\"data row51 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row51_col5\" class=\"data row51 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row51_col6\" class=\"data row51 col6\" >{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row51_col7\" class=\"data row51 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row51_col8\" class=\"data row51 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row52_col0\" class=\"data row52 col0\" >validmind.model_validation.sklearn.ScoreProbabilityAlignment</td>\n",
+              "      <td id=\"T_56dd5_row52_col1\" class=\"data row52 col1\" >Score Probability Alignment</td>\n",
+              "      <td id=\"T_56dd5_row52_col2\" class=\"data row52 col2\" >Analyzes the alignment between credit scores and predicted probabilities....</td>\n",
+              "      <td id=\"T_56dd5_row52_col3\" class=\"data row52 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row52_col4\" class=\"data row52 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row52_col5\" class=\"data row52 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row52_col6\" class=\"data row52 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}</td>\n",
+              "      <td id=\"T_56dd5_row52_col7\" class=\"data row52 col7\" >['visualization', 'credit_risk', 'calibration']</td>\n",
+              "      <td id=\"T_56dd5_row52_col8\" class=\"data row52 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row53_col0\" class=\"data row53 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
+              "      <td id=\"T_56dd5_row53_col1\" class=\"data row53 col1\" >Training Test Degradation</td>\n",
+              "      <td id=\"T_56dd5_row53_col2\" class=\"data row53 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
+              "      <td id=\"T_56dd5_row53_col3\" class=\"data row53 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row53_col4\" class=\"data row53 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row53_col5\" class=\"data row53 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row53_col6\" class=\"data row53 col6\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_56dd5_row53_col7\" class=\"data row53 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row53_col8\" class=\"data row53 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row54_col0\" class=\"data row54 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
+              "      <td id=\"T_56dd5_row54_col1\" class=\"data row54 col1\" >Weakspots Diagnosis</td>\n",
+              "      <td id=\"T_56dd5_row54_col2\" class=\"data row54 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
+              "      <td id=\"T_56dd5_row54_col3\" class=\"data row54 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row54_col4\" class=\"data row54 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row54_col5\" class=\"data row54 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row54_col6\" class=\"data row54 col6\" >{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}</td>\n",
+              "      <td id=\"T_56dd5_row54_col7\" class=\"data row54 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row54_col8\" class=\"data row54 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row55_col0\" class=\"data row55 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
+              "      <td id=\"T_56dd5_row55_col1\" class=\"data row55 col1\" >Cumulative Prediction Probabilities</td>\n",
+              "      <td id=\"T_56dd5_row55_col2\" class=\"data row55 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....</td>\n",
+              "      <td id=\"T_56dd5_row55_col3\" class=\"data row55 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row55_col4\" class=\"data row55 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row55_col5\" class=\"data row55 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row55_col6\" class=\"data row55 col6\" >{'title': {'type': 'str', 'default': 'Cumulative Probabilities'}}</td>\n",
+              "      <td id=\"T_56dd5_row55_col7\" class=\"data row55 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_56dd5_row55_col8\" class=\"data row55 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row56_col0\" class=\"data row56 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
+              "      <td id=\"T_56dd5_row56_col1\" class=\"data row56 col1\" >GINI Table</td>\n",
+              "      <td id=\"T_56dd5_row56_col2\" class=\"data row56 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
+              "      <td id=\"T_56dd5_row56_col3\" class=\"data row56 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row56_col4\" class=\"data row56 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row56_col5\" class=\"data row56 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row56_col6\" class=\"data row56 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row56_col7\" class=\"data row56 col7\" >['model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row56_col8\" class=\"data row56 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row57_col0\" class=\"data row57 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
+              "      <td id=\"T_56dd5_row57_col1\" class=\"data row57 col1\" >Kolmogorov Smirnov</td>\n",
+              "      <td id=\"T_56dd5_row57_col2\" class=\"data row57 col2\" >Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....</td>\n",
+              "      <td id=\"T_56dd5_row57_col3\" class=\"data row57 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row57_col4\" class=\"data row57 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row57_col5\" class=\"data row57 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row57_col6\" class=\"data row57 col6\" >{'dist': {'type': 'str', 'default': 'norm'}}</td>\n",
+              "      <td id=\"T_56dd5_row57_col7\" class=\"data row57 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_56dd5_row57_col8\" class=\"data row57 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row58_col0\" class=\"data row58 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
+              "      <td id=\"T_56dd5_row58_col1\" class=\"data row58 col1\" >Lilliefors</td>\n",
+              "      <td id=\"T_56dd5_row58_col2\" class=\"data row58 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
+              "      <td id=\"T_56dd5_row58_col3\" class=\"data row58 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row58_col4\" class=\"data row58 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row58_col5\" class=\"data row58 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row58_col6\" class=\"data row58 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row58_col7\" class=\"data row58 col7\" >['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']</td>\n",
+              "      <td id=\"T_56dd5_row58_col8\" class=\"data row58 col8\" >['classification', 'regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row59_col0\" class=\"data row59 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
+              "      <td id=\"T_56dd5_row59_col1\" class=\"data row59 col1\" >Prediction Probabilities Histogram</td>\n",
+              "      <td id=\"T_56dd5_row59_col2\" class=\"data row59 col2\" >Assesses the predictive probability distribution for binary classification to evaluate model performance and...</td>\n",
+              "      <td id=\"T_56dd5_row59_col3\" class=\"data row59 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row59_col4\" class=\"data row59 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row59_col5\" class=\"data row59 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row59_col6\" class=\"data row59 col6\" >{'title': {'type': 'str', 'default': 'Histogram of Predictive Probabilities'}}</td>\n",
+              "      <td id=\"T_56dd5_row59_col7\" class=\"data row59 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_56dd5_row59_col8\" class=\"data row59 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row60_col0\" class=\"data row60 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
+              "      <td id=\"T_56dd5_row60_col1\" class=\"data row60 col1\" >Scorecard Histogram</td>\n",
+              "      <td id=\"T_56dd5_row60_col2\" class=\"data row60 col2\" >The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...</td>\n",
+              "      <td id=\"T_56dd5_row60_col3\" class=\"data row60 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row60_col4\" class=\"data row60 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row60_col5\" class=\"data row60 col5\" >['dataset']</td>\n",
+              "      <td id=\"T_56dd5_row60_col6\" class=\"data row60 col6\" >{'title': {'type': 'str', 'default': 'Histogram of Scores'}, 'score_column': {'type': 'str', 'default': 'score'}}</td>\n",
+              "      <td id=\"T_56dd5_row60_col7\" class=\"data row60 col7\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
+              "      <td id=\"T_56dd5_row60_col8\" class=\"data row60 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row61_col0\" class=\"data row61 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
+              "      <td id=\"T_56dd5_row61_col1\" class=\"data row61 col1\" >Calibration Curve Drift</td>\n",
+              "      <td id=\"T_56dd5_row61_col2\" class=\"data row61 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row61_col3\" class=\"data row61 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row61_col4\" class=\"data row61 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row61_col5\" class=\"data row61 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row61_col6\" class=\"data row61 col6\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_56dd5_row61_col7\" class=\"data row61 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row61_col8\" class=\"data row61 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row62_col0\" class=\"data row62 col0\" >validmind.ongoing_monitoring.ClassDiscriminationDrift</td>\n",
+              "      <td id=\"T_56dd5_row62_col1\" class=\"data row62 col1\" >Class Discrimination Drift</td>\n",
+              "      <td id=\"T_56dd5_row62_col2\" class=\"data row62 col2\" >Compares classification discrimination metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row62_col3\" class=\"data row62 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row62_col4\" class=\"data row62 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row62_col5\" class=\"data row62 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row62_col6\" class=\"data row62 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_56dd5_row62_col7\" class=\"data row62 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row62_col8\" class=\"data row62 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row63_col0\" class=\"data row63 col0\" >validmind.ongoing_monitoring.ClassImbalanceDrift</td>\n",
+              "      <td id=\"T_56dd5_row63_col1\" class=\"data row63 col1\" >Class Imbalance Drift</td>\n",
+              "      <td id=\"T_56dd5_row63_col2\" class=\"data row63 col2\" >Evaluates drift in class distribution between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row63_col3\" class=\"data row63 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row63_col4\" class=\"data row63 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row63_col5\" class=\"data row63 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_56dd5_row63_col6\" class=\"data row63 col6\" >{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}</td>\n",
+              "      <td id=\"T_56dd5_row63_col7\" class=\"data row63 col7\" >['tabular_data', 'binary_classification', 'multiclass_classification']</td>\n",
+              "      <td id=\"T_56dd5_row63_col8\" class=\"data row63 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row64_col0\" class=\"data row64 col0\" >validmind.ongoing_monitoring.ClassificationAccuracyDrift</td>\n",
+              "      <td id=\"T_56dd5_row64_col1\" class=\"data row64 col1\" >Classification Accuracy Drift</td>\n",
+              "      <td id=\"T_56dd5_row64_col2\" class=\"data row64 col2\" >Compares classification accuracy metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row64_col3\" class=\"data row64 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row64_col4\" class=\"data row64 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row64_col5\" class=\"data row64 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row64_col6\" class=\"data row64 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_56dd5_row64_col7\" class=\"data row64 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row64_col8\" class=\"data row64 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row65_col0\" class=\"data row65 col0\" >validmind.ongoing_monitoring.ConfusionMatrixDrift</td>\n",
+              "      <td id=\"T_56dd5_row65_col1\" class=\"data row65 col1\" >Confusion Matrix Drift</td>\n",
+              "      <td id=\"T_56dd5_row65_col2\" class=\"data row65 col2\" >Compares confusion matrix metrics between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row65_col3\" class=\"data row65 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row65_col4\" class=\"data row65 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row65_col5\" class=\"data row65 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row65_col6\" class=\"data row65 col6\" >{'drift_pct_threshold': {'type': '_empty', 'default': 20}}</td>\n",
+              "      <td id=\"T_56dd5_row65_col7\" class=\"data row65 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']</td>\n",
+              "      <td id=\"T_56dd5_row65_col8\" class=\"data row65 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row66_col0\" class=\"data row66 col0\" >validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift</td>\n",
+              "      <td id=\"T_56dd5_row66_col1\" class=\"data row66 col1\" >Cumulative Prediction Probabilities Drift</td>\n",
+              "      <td id=\"T_56dd5_row66_col2\" class=\"data row66 col2\" >Compares cumulative prediction probability distributions between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row66_col3\" class=\"data row66 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row66_col4\" class=\"data row66 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row66_col5\" class=\"data row66 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row66_col6\" class=\"data row66 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row66_col7\" class=\"data row66 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_56dd5_row66_col8\" class=\"data row66 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row67_col0\" class=\"data row67 col0\" >validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift</td>\n",
+              "      <td id=\"T_56dd5_row67_col1\" class=\"data row67 col1\" >Prediction Probabilities Histogram Drift</td>\n",
+              "      <td id=\"T_56dd5_row67_col2\" class=\"data row67 col2\" >Compares prediction probability distributions between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row67_col3\" class=\"data row67 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row67_col4\" class=\"data row67 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row67_col5\" class=\"data row67 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row67_col6\" class=\"data row67 col6\" >{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_56dd5_row67_col7\" class=\"data row67 col7\" >['visualization', 'credit_risk']</td>\n",
+              "      <td id=\"T_56dd5_row67_col8\" class=\"data row67 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row68_col0\" class=\"data row68 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
+              "      <td id=\"T_56dd5_row68_col1\" class=\"data row68 col1\" >ROC Curve Drift</td>\n",
+              "      <td id=\"T_56dd5_row68_col2\" class=\"data row68 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_56dd5_row68_col3\" class=\"data row68 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row68_col4\" class=\"data row68 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row68_col5\" class=\"data row68 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row68_col6\" class=\"data row68 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row68_col7\" class=\"data row68 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_56dd5_row68_col8\" class=\"data row68 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row69_col0\" class=\"data row69 col0\" >validmind.ongoing_monitoring.ScoreBandsDrift</td>\n",
+              "      <td id=\"T_56dd5_row69_col1\" class=\"data row69 col1\" >Score Bands Drift</td>\n",
+              "      <td id=\"T_56dd5_row69_col2\" class=\"data row69 col2\" >Analyzes drift in population distribution and default rates across score bands....</td>\n",
+              "      <td id=\"T_56dd5_row69_col3\" class=\"data row69 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row69_col4\" class=\"data row69 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row69_col5\" class=\"data row69 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row69_col6\" class=\"data row69 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_56dd5_row69_col7\" class=\"data row69 col7\" >['visualization', 'credit_risk', 'scorecard']</td>\n",
+              "      <td id=\"T_56dd5_row69_col8\" class=\"data row69 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row70_col0\" class=\"data row70 col0\" >validmind.ongoing_monitoring.ScorecardHistogramDrift</td>\n",
+              "      <td id=\"T_56dd5_row70_col1\" class=\"data row70 col1\" >Scorecard Histogram Drift</td>\n",
+              "      <td id=\"T_56dd5_row70_col2\" class=\"data row70 col2\" >Compares score distributions between reference and monitoring datasets for each class....</td>\n",
+              "      <td id=\"T_56dd5_row70_col3\" class=\"data row70 col3\" >True</td>\n",
+              "      <td id=\"T_56dd5_row70_col4\" class=\"data row70 col4\" >True</td>\n",
+              "      <td id=\"T_56dd5_row70_col5\" class=\"data row70 col5\" >['datasets']</td>\n",
+              "      <td id=\"T_56dd5_row70_col6\" class=\"data row70 col6\" >{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}</td>\n",
+              "      <td id=\"T_56dd5_row70_col7\" class=\"data row70 col7\" >['visualization', 'credit_risk', 'logistic_regression']</td>\n",
+              "      <td id=\"T_56dd5_row70_col8\" class=\"data row70 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row71_col0\" class=\"data row71 col0\" >validmind.unit_metrics.classification.Accuracy</td>\n",
+              "      <td id=\"T_56dd5_row71_col1\" class=\"data row71 col1\" >Accuracy</td>\n",
+              "      <td id=\"T_56dd5_row71_col2\" class=\"data row71 col2\" >Calculates the accuracy of a model</td>\n",
+              "      <td id=\"T_56dd5_row71_col3\" class=\"data row71 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row71_col4\" class=\"data row71 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row71_col5\" class=\"data row71 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_56dd5_row71_col6\" class=\"data row71 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row71_col7\" class=\"data row71 col7\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row71_col8\" class=\"data row71 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row72_col0\" class=\"data row72 col0\" >validmind.unit_metrics.classification.F1</td>\n",
+              "      <td id=\"T_56dd5_row72_col1\" class=\"data row72 col1\" >F1</td>\n",
+              "      <td id=\"T_56dd5_row72_col2\" class=\"data row72 col2\" >Calculates the F1 score for a classification model.</td>\n",
+              "      <td id=\"T_56dd5_row72_col3\" class=\"data row72 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row72_col4\" class=\"data row72 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row72_col5\" class=\"data row72 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row72_col6\" class=\"data row72 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row72_col7\" class=\"data row72 col7\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row72_col8\" class=\"data row72 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row73_col0\" class=\"data row73 col0\" >validmind.unit_metrics.classification.Precision</td>\n",
+              "      <td id=\"T_56dd5_row73_col1\" class=\"data row73 col1\" >Precision</td>\n",
+              "      <td id=\"T_56dd5_row73_col2\" class=\"data row73 col2\" >Calculates the precision for a classification model.</td>\n",
+              "      <td id=\"T_56dd5_row73_col3\" class=\"data row73 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row73_col4\" class=\"data row73 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row73_col5\" class=\"data row73 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row73_col6\" class=\"data row73 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row73_col7\" class=\"data row73 col7\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row73_col8\" class=\"data row73 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row74_col0\" class=\"data row74 col0\" >validmind.unit_metrics.classification.ROC_AUC</td>\n",
+              "      <td id=\"T_56dd5_row74_col1\" class=\"data row74 col1\" >ROC AUC</td>\n",
+              "      <td id=\"T_56dd5_row74_col2\" class=\"data row74 col2\" >Calculates the ROC AUC for a classification model.</td>\n",
+              "      <td id=\"T_56dd5_row74_col3\" class=\"data row74 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row74_col4\" class=\"data row74 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row74_col5\" class=\"data row74 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row74_col6\" class=\"data row74 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row74_col7\" class=\"data row74 col7\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row74_col8\" class=\"data row74 col8\" >['classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_56dd5_row75_col0\" class=\"data row75 col0\" >validmind.unit_metrics.classification.Recall</td>\n",
+              "      <td id=\"T_56dd5_row75_col1\" class=\"data row75 col1\" >Recall</td>\n",
+              "      <td id=\"T_56dd5_row75_col2\" class=\"data row75 col2\" >Calculates the recall for a classification model.</td>\n",
+              "      <td id=\"T_56dd5_row75_col3\" class=\"data row75 col3\" >False</td>\n",
+              "      <td id=\"T_56dd5_row75_col4\" class=\"data row75 col4\" >False</td>\n",
+              "      <td id=\"T_56dd5_row75_col5\" class=\"data row75 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_56dd5_row75_col6\" class=\"data row75 col6\" >{}</td>\n",
+              "      <td id=\"T_56dd5_row75_col7\" class=\"data row75 col7\" >['classification']</td>\n",
+              "      <td id=\"T_56dd5_row75_col8\" class=\"data row75 col8\" >['classification']</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x103a27bb0>"
+              "<pandas.io.formats.style.Styler at 0x10516c880>"
             ]
           },
           "execution_count": 7,
@@ -3321,94 +3955,110 @@
           "data": {
             "text/html": [
               "<style type=\"text/css\">\n",
-              "#T_5df1f th {\n",
+              "#T_4d8bf th {\n",
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-              "#T_5df1f_row0_col0, #T_5df1f_row0_col1, #T_5df1f_row0_col2, #T_5df1f_row0_col3, #T_5df1f_row0_col4, #T_5df1f_row0_col5, #T_5df1f_row0_col6, #T_5df1f_row1_col0, #T_5df1f_row1_col1, #T_5df1f_row1_col2, #T_5df1f_row1_col3, #T_5df1f_row1_col4, #T_5df1f_row1_col5, #T_5df1f_row1_col6, #T_5df1f_row2_col0, #T_5df1f_row2_col1, #T_5df1f_row2_col2, #T_5df1f_row2_col3, #T_5df1f_row2_col4, #T_5df1f_row2_col5, #T_5df1f_row2_col6, #T_5df1f_row3_col0, #T_5df1f_row3_col1, #T_5df1f_row3_col2, #T_5df1f_row3_col3, #T_5df1f_row3_col4, #T_5df1f_row3_col5, #T_5df1f_row3_col6, #T_5df1f_row4_col0, #T_5df1f_row4_col1, #T_5df1f_row4_col2, #T_5df1f_row4_col3, #T_5df1f_row4_col4, #T_5df1f_row4_col5, #T_5df1f_row4_col6, #T_5df1f_row5_col0, #T_5df1f_row5_col1, #T_5df1f_row5_col2, #T_5df1f_row5_col3, #T_5df1f_row5_col4, #T_5df1f_row5_col5, #T_5df1f_row5_col6, #T_5df1f_row6_col0, #T_5df1f_row6_col1, #T_5df1f_row6_col2, #T_5df1f_row6_col3, #T_5df1f_row6_col4, #T_5df1f_row6_col5, #T_5df1f_row6_col6 {\n",
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               "}\n",
               "</style>\n",
-              "<table id=\"T_5df1f\">\n",
+              "<table id=\"T_4d8bf\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_5df1f_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_5df1f_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_5df1f_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_5df1f_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_5df1f_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
-              "      <th id=\"T_5df1f_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
-              "      <th id=\"T_5df1f_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
+              "      <th id=\"T_4d8bf_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
+              "      <th id=\"T_4d8bf_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
+              "      <th id=\"T_4d8bf_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+              "      <th id=\"T_4d8bf_level0_col3\" class=\"col_heading level0 col3\" >Has Figure</th>\n",
+              "      <th id=\"T_4d8bf_level0_col4\" class=\"col_heading level0 col4\" >Has Table</th>\n",
+              "      <th id=\"T_4d8bf_level0_col5\" class=\"col_heading level0 col5\" >Required Inputs</th>\n",
+              "      <th id=\"T_4d8bf_level0_col6\" class=\"col_heading level0 col6\" >Params</th>\n",
+              "      <th id=\"T_4d8bf_level0_col7\" class=\"col_heading level0 col7\" >Tags</th>\n",
+              "      <th id=\"T_4d8bf_level0_col8\" class=\"col_heading level0 col8\" >Tasks</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_5df1f_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
-              "      <td id=\"T_5df1f_row0_col1\" class=\"data row0 col1\" >Regression Residuals Plot</td>\n",
-              "      <td id=\"T_5df1f_row0_col2\" class=\"data row0 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
-              "      <td id=\"T_5df1f_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_5df1f_row0_col4\" class=\"data row0 col4\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_5df1f_row0_col5\" class=\"data row0 col5\" >['model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row0_col6\" class=\"data row0 col6\" >['regression']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_5df1f_row1_col1\" class=\"data row1 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_5df1f_row1_col2\" class=\"data row1 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_5df1f_row1_col3\" class=\"data row1 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_5df1f_row1_col4\" class=\"data row1 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_5df1f_row1_col5\" class=\"data row1 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row1_col6\" class=\"data row1 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_5df1f_row2_col1\" class=\"data row2 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_5df1f_row2_col2\" class=\"data row2 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_5df1f_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_5df1f_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
-              "      <td id=\"T_5df1f_row2_col5\" class=\"data row2 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row2_col6\" class=\"data row2 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_5df1f_row3_col1\" class=\"data row3 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_5df1f_row3_col2\" class=\"data row3 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_5df1f_row3_col3\" class=\"data row3 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_5df1f_row3_col4\" class=\"data row3 col4\" >{}</td>\n",
-              "      <td id=\"T_5df1f_row3_col5\" class=\"data row3 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row3_col6\" class=\"data row3 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_5df1f_row4_col1\" class=\"data row4 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_5df1f_row4_col2\" class=\"data row4 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_5df1f_row4_col3\" class=\"data row4 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_5df1f_row4_col4\" class=\"data row4 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_5df1f_row4_col5\" class=\"data row4 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row4_col6\" class=\"data row4 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
-              "      <td id=\"T_5df1f_row5_col1\" class=\"data row5 col1\" >Calibration Curve Drift</td>\n",
-              "      <td id=\"T_5df1f_row5_col2\" class=\"data row5 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_5df1f_row5_col3\" class=\"data row5 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_5df1f_row5_col4\" class=\"data row5 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
-              "      <td id=\"T_5df1f_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_5df1f_row6_col0\" class=\"data row6 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
-              "      <td id=\"T_5df1f_row6_col1\" class=\"data row6 col1\" >ROC Curve Drift</td>\n",
-              "      <td id=\"T_5df1f_row6_col2\" class=\"data row6 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_5df1f_row6_col3\" class=\"data row6 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_5df1f_row6_col4\" class=\"data row6 col4\" >{}</td>\n",
-              "      <td id=\"T_5df1f_row6_col5\" class=\"data row6 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_5df1f_row6_col6\" class=\"data row6 col6\" >['classification', 'text_classification']</td>\n",
+              "      <td id=\"T_4d8bf_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
+              "      <td id=\"T_4d8bf_row0_col1\" class=\"data row0 col1\" >Regression Residuals Plot</td>\n",
+              "      <td id=\"T_4d8bf_row0_col2\" class=\"data row0 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
+              "      <td id=\"T_4d8bf_row0_col3\" class=\"data row0 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row0_col4\" class=\"data row0 col4\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row0_col5\" class=\"data row0 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_4d8bf_row0_col6\" class=\"data row0 col6\" >{'bin_size': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_4d8bf_row0_col7\" class=\"data row0 col7\" >['model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row0_col8\" class=\"data row0 col8\" >['regression']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
+              "      <td id=\"T_4d8bf_row1_col1\" class=\"data row1 col1\" >Confusion Matrix</td>\n",
+              "      <td id=\"T_4d8bf_row1_col2\" class=\"data row1 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
+              "      <td id=\"T_4d8bf_row1_col3\" class=\"data row1 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row1_col4\" class=\"data row1 col4\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row1_col5\" class=\"data row1 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_4d8bf_row1_col6\" class=\"data row1 col6\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_4d8bf_row1_col7\" class=\"data row1 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row1_col8\" class=\"data row1 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
+              "      <td id=\"T_4d8bf_row2_col1\" class=\"data row2 col1\" >Precision Recall Curve</td>\n",
+              "      <td id=\"T_4d8bf_row2_col2\" class=\"data row2 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
+              "      <td id=\"T_4d8bf_row2_col3\" class=\"data row2 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row2_col4\" class=\"data row2 col4\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row2_col5\" class=\"data row2 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_4d8bf_row2_col6\" class=\"data row2 col6\" >{}</td>\n",
+              "      <td id=\"T_4d8bf_row2_col7\" class=\"data row2 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row2_col8\" class=\"data row2 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
+              "      <td id=\"T_4d8bf_row3_col1\" class=\"data row3 col1\" >ROC Curve</td>\n",
+              "      <td id=\"T_4d8bf_row3_col2\" class=\"data row3 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
+              "      <td id=\"T_4d8bf_row3_col3\" class=\"data row3 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row3_col4\" class=\"data row3 col4\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row3_col5\" class=\"data row3 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_4d8bf_row3_col6\" class=\"data row3 col6\" >{}</td>\n",
+              "      <td id=\"T_4d8bf_row3_col7\" class=\"data row3 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row3_col8\" class=\"data row3 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
+              "      <td id=\"T_4d8bf_row4_col1\" class=\"data row4 col1\" >Training Test Degradation</td>\n",
+              "      <td id=\"T_4d8bf_row4_col2\" class=\"data row4 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
+              "      <td id=\"T_4d8bf_row4_col3\" class=\"data row4 col3\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row4_col4\" class=\"data row4 col4\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row4_col5\" class=\"data row4 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_4d8bf_row4_col6\" class=\"data row4 col6\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_4d8bf_row4_col7\" class=\"data row4 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row4_col8\" class=\"data row4 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
+              "      <td id=\"T_4d8bf_row5_col1\" class=\"data row5 col1\" >Calibration Curve Drift</td>\n",
+              "      <td id=\"T_4d8bf_row5_col2\" class=\"data row5 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_4d8bf_row5_col3\" class=\"data row5 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row5_col4\" class=\"data row5 col4\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row5_col5\" class=\"data row5 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_4d8bf_row5_col6\" class=\"data row5 col6\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_4d8bf_row5_col7\" class=\"data row5 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row5_col8\" class=\"data row5 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_4d8bf_row6_col0\" class=\"data row6 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
+              "      <td id=\"T_4d8bf_row6_col1\" class=\"data row6 col1\" >ROC Curve Drift</td>\n",
+              "      <td id=\"T_4d8bf_row6_col2\" class=\"data row6 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_4d8bf_row6_col3\" class=\"data row6 col3\" >True</td>\n",
+              "      <td id=\"T_4d8bf_row6_col4\" class=\"data row6 col4\" >False</td>\n",
+              "      <td id=\"T_4d8bf_row6_col5\" class=\"data row6 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_4d8bf_row6_col6\" class=\"data row6 col6\" >{}</td>\n",
+              "      <td id=\"T_4d8bf_row6_col7\" class=\"data row6 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_4d8bf_row6_col8\" class=\"data row6 col8\" >['classification', 'text_classification']</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x37563ceb0>"
+              "<pandas.io.formats.style.Styler at 0x36a280f40>"
             ]
           },
           "execution_count": 8,
@@ -3436,85 +4086,99 @@
           "data": {
             "text/html": [
               "<style type=\"text/css\">\n",
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+              "#T_36394 th {\n",
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-              "<table id=\"T_6455a\">\n",
+              "<table id=\"T_36394\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_6455a_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_6455a_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_6455a_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_6455a_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_6455a_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\n",
-              "      <th id=\"T_6455a_level0_col5\" class=\"col_heading level0 col5\" >Tags</th>\n",
-              "      <th id=\"T_6455a_level0_col6\" class=\"col_heading level0 col6\" >Tasks</th>\n",
+              "      <th id=\"T_36394_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
+              "      <th id=\"T_36394_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
+              "      <th id=\"T_36394_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+              "      <th id=\"T_36394_level0_col3\" class=\"col_heading level0 col3\" >Has Figure</th>\n",
+              "      <th id=\"T_36394_level0_col4\" class=\"col_heading level0 col4\" >Has Table</th>\n",
+              "      <th id=\"T_36394_level0_col5\" class=\"col_heading level0 col5\" >Required Inputs</th>\n",
+              "      <th id=\"T_36394_level0_col6\" class=\"col_heading level0 col6\" >Params</th>\n",
+              "      <th id=\"T_36394_level0_col7\" class=\"col_heading level0 col7\" >Tags</th>\n",
+              "      <th id=\"T_36394_level0_col8\" class=\"col_heading level0 col8\" >Tasks</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_6455a_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_6455a_row0_col1\" class=\"data row0 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_6455a_row0_col2\" class=\"data row0 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_6455a_row0_col3\" class=\"data row0 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_6455a_row0_col4\" class=\"data row0 col4\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
-              "      <td id=\"T_6455a_row0_col5\" class=\"data row0 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row0_col6\" class=\"data row0 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_6455a_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_6455a_row1_col1\" class=\"data row1 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_6455a_row1_col2\" class=\"data row1 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_6455a_row1_col3\" class=\"data row1 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_6455a_row1_col4\" class=\"data row1 col4\" >{}</td>\n",
-              "      <td id=\"T_6455a_row1_col5\" class=\"data row1 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row1_col6\" class=\"data row1 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_6455a_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_6455a_row2_col1\" class=\"data row2 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_6455a_row2_col2\" class=\"data row2 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_6455a_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_6455a_row2_col4\" class=\"data row2 col4\" >{}</td>\n",
-              "      <td id=\"T_6455a_row2_col5\" class=\"data row2 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row2_col6\" class=\"data row2 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_6455a_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_6455a_row3_col1\" class=\"data row3 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_6455a_row3_col2\" class=\"data row3 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_6455a_row3_col3\" class=\"data row3 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_6455a_row3_col4\" class=\"data row3 col4\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
-              "      <td id=\"T_6455a_row3_col5\" class=\"data row3 col5\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row3_col6\" class=\"data row3 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_6455a_row4_col0\" class=\"data row4 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
-              "      <td id=\"T_6455a_row4_col1\" class=\"data row4 col1\" >Calibration Curve Drift</td>\n",
-              "      <td id=\"T_6455a_row4_col2\" class=\"data row4 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_6455a_row4_col3\" class=\"data row4 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_6455a_row4_col4\" class=\"data row4 col4\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
-              "      <td id=\"T_6455a_row4_col5\" class=\"data row4 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row4_col6\" class=\"data row4 col6\" >['classification', 'text_classification']</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_6455a_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
-              "      <td id=\"T_6455a_row5_col1\" class=\"data row5 col1\" >ROC Curve Drift</td>\n",
-              "      <td id=\"T_6455a_row5_col2\" class=\"data row5 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
-              "      <td id=\"T_6455a_row5_col3\" class=\"data row5 col3\" >['datasets', 'model']</td>\n",
-              "      <td id=\"T_6455a_row5_col4\" class=\"data row5 col4\" >{}</td>\n",
-              "      <td id=\"T_6455a_row5_col5\" class=\"data row5 col5\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
-              "      <td id=\"T_6455a_row5_col6\" class=\"data row5 col6\" >['classification', 'text_classification']</td>\n",
+              "      <td id=\"T_36394_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
+              "      <td id=\"T_36394_row0_col1\" class=\"data row0 col1\" >Confusion Matrix</td>\n",
+              "      <td id=\"T_36394_row0_col2\" class=\"data row0 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
+              "      <td id=\"T_36394_row0_col3\" class=\"data row0 col3\" >True</td>\n",
+              "      <td id=\"T_36394_row0_col4\" class=\"data row0 col4\" >False</td>\n",
+              "      <td id=\"T_36394_row0_col5\" class=\"data row0 col5\" >['dataset', 'model']</td>\n",
+              "      <td id=\"T_36394_row0_col6\" class=\"data row0 col6\" >{'threshold': {'type': 'float', 'default': 0.5}}</td>\n",
+              "      <td id=\"T_36394_row0_col7\" class=\"data row0 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row0_col8\" class=\"data row0 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_36394_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
+              "      <td id=\"T_36394_row1_col1\" class=\"data row1 col1\" >Precision Recall Curve</td>\n",
+              "      <td id=\"T_36394_row1_col2\" class=\"data row1 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
+              "      <td id=\"T_36394_row1_col3\" class=\"data row1 col3\" >True</td>\n",
+              "      <td id=\"T_36394_row1_col4\" class=\"data row1 col4\" >False</td>\n",
+              "      <td id=\"T_36394_row1_col5\" class=\"data row1 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_36394_row1_col6\" class=\"data row1 col6\" >{}</td>\n",
+              "      <td id=\"T_36394_row1_col7\" class=\"data row1 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row1_col8\" class=\"data row1 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_36394_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
+              "      <td id=\"T_36394_row2_col1\" class=\"data row2 col1\" >ROC Curve</td>\n",
+              "      <td id=\"T_36394_row2_col2\" class=\"data row2 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
+              "      <td id=\"T_36394_row2_col3\" class=\"data row2 col3\" >True</td>\n",
+              "      <td id=\"T_36394_row2_col4\" class=\"data row2 col4\" >False</td>\n",
+              "      <td id=\"T_36394_row2_col5\" class=\"data row2 col5\" >['model', 'dataset']</td>\n",
+              "      <td id=\"T_36394_row2_col6\" class=\"data row2 col6\" >{}</td>\n",
+              "      <td id=\"T_36394_row2_col7\" class=\"data row2 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row2_col8\" class=\"data row2 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_36394_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
+              "      <td id=\"T_36394_row3_col1\" class=\"data row3 col1\" >Training Test Degradation</td>\n",
+              "      <td id=\"T_36394_row3_col2\" class=\"data row3 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
+              "      <td id=\"T_36394_row3_col3\" class=\"data row3 col3\" >False</td>\n",
+              "      <td id=\"T_36394_row3_col4\" class=\"data row3 col4\" >True</td>\n",
+              "      <td id=\"T_36394_row3_col5\" class=\"data row3 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_36394_row3_col6\" class=\"data row3 col6\" >{'max_threshold': {'type': 'float', 'default': 0.1}}</td>\n",
+              "      <td id=\"T_36394_row3_col7\" class=\"data row3 col7\" >['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row3_col8\" class=\"data row3 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_36394_row4_col0\" class=\"data row4 col0\" >validmind.ongoing_monitoring.CalibrationCurveDrift</td>\n",
+              "      <td id=\"T_36394_row4_col1\" class=\"data row4 col1\" >Calibration Curve Drift</td>\n",
+              "      <td id=\"T_36394_row4_col2\" class=\"data row4 col2\" >Evaluates changes in probability calibration between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_36394_row4_col3\" class=\"data row4 col3\" >True</td>\n",
+              "      <td id=\"T_36394_row4_col4\" class=\"data row4 col4\" >True</td>\n",
+              "      <td id=\"T_36394_row4_col5\" class=\"data row4 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_36394_row4_col6\" class=\"data row4 col6\" >{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}</td>\n",
+              "      <td id=\"T_36394_row4_col7\" class=\"data row4 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row4_col8\" class=\"data row4 col8\" >['classification', 'text_classification']</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td id=\"T_36394_row5_col0\" class=\"data row5 col0\" >validmind.ongoing_monitoring.ROCCurveDrift</td>\n",
+              "      <td id=\"T_36394_row5_col1\" class=\"data row5 col1\" >ROC Curve Drift</td>\n",
+              "      <td id=\"T_36394_row5_col2\" class=\"data row5 col2\" >Compares ROC curves between reference and monitoring datasets....</td>\n",
+              "      <td id=\"T_36394_row5_col3\" class=\"data row5 col3\" >True</td>\n",
+              "      <td id=\"T_36394_row5_col4\" class=\"data row5 col4\" >False</td>\n",
+              "      <td id=\"T_36394_row5_col5\" class=\"data row5 col5\" >['datasets', 'model']</td>\n",
+              "      <td id=\"T_36394_row5_col6\" class=\"data row5 col6\" >{}</td>\n",
+              "      <td id=\"T_36394_row5_col7\" class=\"data row5 col7\" >['sklearn', 'binary_classification', 'model_performance', 'visualization']</td>\n",
+              "      <td id=\"T_36394_row5_col8\" class=\"data row5 col8\" >['classification', 'text_classification']</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x375608640>"
+              "<pandas.io.formats.style.Styler at 0x380009c40>"
             ]
           },
           "execution_count": 9,
@@ -3616,7 +4280,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "5025f3a7dbb34f4c9de1b26e4909f3f7",
+              "model_id": "41899643f5a5460ca23e60681f09d81c",
               "version_major": 2,
               "version_minor": 0
             },
diff --git a/scripts/bulk_ai_test_updates.py b/scripts/bulk_ai_test_updates.py
index d44a7136f..6fbc82a95 100644
--- a/scripts/bulk_ai_test_updates.py
+++ b/scripts/bulk_ai_test_updates.py
@@ -264,6 +264,98 @@ def ExampleConfusionMatrix(model: VMModel, dataset: VMDataset):
 DO NOT CHANGE ANYTHING OTHER THAN ADDING THE NEW RAW DATA MECHANISM... I.E. DO NOT REMOVE ANYTHING FROM THE RETURN TUPLE OR THE RETURN VALUE (if it is a single object)
 """
 
+add_return_type_prompt = """
+You are an expert Python engineer and data scientist with broad experience across many domains.
+ValidMind is a company that provides a Python SDK for building and running tests for the purposes of model risk management.
+ValidMind's SDK offers a library of "test" functions that are run with our test harness against many types of models and datasets.
+
+Your task is to analyze the test function and add appropriate return type annotations to the function signature.
+
+CRITICAL: DO NOT CHANGE ANYTHING IN THE CODE EXCEPT:
+1. Adding the return type annotation to the function signature
+2. Adding any necessary import statements WITH THE EXISTING IMPORTS (do not add imports elsewhere)
+
+EXTREMELY IMPORTANT: ALWAYS PRESERVE COPYRIGHT AND LICENSE INFORMATION AT THE TOP OF THE FILE!
+You must include any copyright, license, and SPDX identifier lines from the original file!
+
+ValidMind test functions return either a single object or a tuple of objects.
+These objects are turned into a test result report by the test harness.
+They can return any number of the following types of objects:
+- Tables (pd.DataFrame or List[Dict[str, Any]])
+- Figures (matplotlib.figure.Figure, plotly.graph_objects.Figure (go.Figure), or List of these)
+- Values (scalar values like float, int, str, or container types like List, Dict)
+- Pass/Fail (bool value indicating whether the test passed or failed)
+- Raw Data (RawData object containing intermediate data)
+
+Common imports that might be needed in the return type annotation:
+- from typing import Any, Dict, List, Tuple, Union, Optional
+- import plotly.graph_objects as go
+- import matplotlib.figure
+- import pandas as pd
+- from validmind import RawData
+
+You should inspect the return statement(s) in the function to determine what the function actually returns.
+Then, add the appropriate return type annotation to the function signature.
+
+If the function already has a return type annotation, don't change it - in this case, return the original code without any changes.
+
+Examples:
+
+1. For a function that returns a single figure:
+```python
+def PlotHistogram(dataset: VMDataset):
+    # ... code ...
+    return fig
+```
+Should become:
+```python
+def PlotHistogram(dataset: VMDataset) -> go.Figure:
+    # ... code ...
+    return fig
+```
+
+2. For a function that returns multiple objects in a tuple:
+```python
+def ClassImbalance(dataset: VMDataset):
+    # ... code ...
+    return stats, fig, passed
+```
+Should become:
+```python
+def ClassImbalance(dataset: VMDataset) -> Tuple[Dict[str, Any], go.Figure, bool]:
+    # ... code ...
+    return stats, fig, passed
+```
+
+3. For a function that builds a list of figures and returns it as a tuple:
+```python
+def MultiplePlots(dataset: VMDataset):
+    # ... code ...
+    returns = []
+    returns.append(fig1)
+    returns.append(fig2)
+    returns.append(RawData(...))
+    return tuple(returns)
+```
+Should become:
+```python
+def MultiplePlots(dataset: VMDataset) -> Tuple[go.Figure, go.Figure, RawData]:
+    # ... code ...
+    returns = []
+    returns.append(fig1)
+    returns.append(fig2)
+    returns.append(RawData(...))
+    return tuple(returns)
+```
+
+Return only the updated code and nothing else.
+Do not wrap the code in backticks, simply return valid Python code.
+Only add the correct imports if they are not already present in the file, and place them with the existing imports.
+DO NOT modify the function body in any way - the only changes should be to the function signature and possibly adding imports.
+NEVER REMOVE COPYRIGHT NOTICES OR LICENSE INFORMATION!
+If the function already has a return type annotation, return the original code without any changes.
+""".strip()
+
 custom_prompt_system = """
 You are an expert Python engineer and data scientist with broad experience across many domains.
 ValidMind is a company that provides a Python SDK for building and running tests for the purposes of model risk management.
@@ -394,6 +486,31 @@ def add_raw_data_to_test(path):
         f.write(updated_file_contents)
 
 
+def add_return_type_to_test(path):
+    """Add return type annotation to a test function"""
+    # get file contents from path
+    click.echo(f"> {path}")
+    with open(path, "r") as f:
+        file_contents = f.read()
+
+    response = client.chat.completions.create(
+        model=OPENAI_GPT_MODEL,
+        messages=[
+            {"role": "system", "content": add_return_type_prompt},
+            {"role": "user", "content": f"```python\n{file_contents}```"},
+        ],
+    )
+
+    updated_file_contents = response.choices[0].message.content
+    # remove starting "```python" and ending "```"
+    updated_file_contents = (
+        updated_file_contents.lstrip("```python").rstrip("```").strip()
+    )
+
+    with open(path, "w") as f:
+        f.write(updated_file_contents)
+
+
 def custom_prompt(path, user_prompt):
     """Custom prompt for a test file"""
     # get file contents from path
@@ -461,7 +578,13 @@ def _is_test_file(path):
 @click.option(
     "--action",
     type=click.Choice(
-        ["add_description", "add_raw_data", "custom_prompt", "custom_review"]
+        [
+            "add_description",
+            "add_raw_data",
+            "add_return_type",
+            "custom_prompt",
+            "custom_review",
+        ]
     ),
     required=True,
 )
@@ -494,6 +617,8 @@ def main(action, path, model):
         func = add_description_to_test
     elif action == "add_raw_data":
         func = add_raw_data_to_test
+    elif action == "add_return_type":
+        func = add_return_type_to_test
     elif action == "custom_prompt":
         if not USER_PROMPT:
             user_prompt = input("Enter your prompt: ")
diff --git a/validmind/tests/data_validation/ACFandPACFPlot.py b/validmind/tests/data_validation/ACFandPACFPlot.py
index c2b6adbc1..5f5d6f397 100644
--- a/validmind/tests/data_validation/ACFandPACFPlot.py
+++ b/validmind/tests/data_validation/ACFandPACFPlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 from statsmodels.tsa.stattools import acf, pacf
@@ -12,7 +14,7 @@
 
 @tags("time_series_data", "forecasting", "statistical_test", "visualization")
 @tasks("regression")
-def ACFandPACFPlot(dataset: VMDataset):
+def ACFandPACFPlot(dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to
     reveal trends and correlations.
diff --git a/validmind/tests/data_validation/ADF.py b/validmind/tests/data_validation/ADF.py
index 747f99181..493556e0e 100644
--- a/validmind/tests/data_validation/ADF.py
+++ b/validmind/tests/data_validation/ADF.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict
+
 import pandas as pd
 from statsmodels.tsa.stattools import adfuller
 
@@ -16,7 +18,7 @@
     "time_series_data", "statsmodels", "forecasting", "statistical_test", "stationarity"
 )
 @tasks("regression")
-def ADF(dataset: VMDataset):
+def ADF(dataset: VMDataset) -> Dict[str, pd.DataFrame]:
     """
     Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test.
 
diff --git a/validmind/tests/data_validation/AutoAR.py b/validmind/tests/data_validation/AutoAR.py
index 94a0df9f2..860c0a40e 100644
--- a/validmind/tests/data_validation/AutoAR.py
+++ b/validmind/tests/data_validation/AutoAR.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict
+
 import pandas as pd
 from statsmodels.tsa.ar_model import AutoReg
 from statsmodels.tsa.stattools import adfuller
@@ -15,7 +17,7 @@
 
 @tags("time_series_data", "statsmodels", "forecasting", "statistical_test")
 @tasks("regression")
-def AutoAR(dataset: VMDataset, max_ar_order: int = 3):
+def AutoAR(dataset: VMDataset, max_ar_order: int = 3) -> Dict[str, pd.DataFrame]:
     """
     Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria.
 
diff --git a/validmind/tests/data_validation/AutoMA.py b/validmind/tests/data_validation/AutoMA.py
index 1c28ee7de..5898da1cc 100644
--- a/validmind/tests/data_validation/AutoMA.py
+++ b/validmind/tests/data_validation/AutoMA.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, Tuple
+
 import pandas as pd
 from statsmodels.tsa.arima.model import ARIMA
 from statsmodels.tsa.stattools import adfuller
@@ -15,7 +17,9 @@
 
 @tags("time_series_data", "statsmodels", "forecasting", "statistical_test")
 @tasks("regression")
-def AutoMA(dataset: VMDataset, max_ma_order: int = 3):
+def AutoMA(
+    dataset: VMDataset, max_ma_order: int = 3
+) -> Tuple[Dict[str, pd.DataFrame], RawData]:
     """
     Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on
     minimal BIC and AIC values.
diff --git a/validmind/tests/data_validation/AutoStationarity.py b/validmind/tests/data_validation/AutoStationarity.py
index e4d857353..a3bef7aad 100644
--- a/validmind/tests/data_validation/AutoStationarity.py
+++ b/validmind/tests/data_validation/AutoStationarity.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict
+
 import numpy as np
 import pandas as pd
 from statsmodels.tsa.stattools import adfuller
@@ -12,7 +14,9 @@
 
 @tags("time_series_data", "statsmodels", "forecasting", "statistical_test")
 @tasks("regression")
-def AutoStationarity(dataset: VMDataset, max_order: int = 5, threshold: float = 0.05):
+def AutoStationarity(
+    dataset: VMDataset, max_order: int = 5, threshold: float = 0.05
+) -> Dict[str, pd.DataFrame]:
     """
     Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame.
 
diff --git a/validmind/tests/data_validation/BivariateScatterPlots.py b/validmind/tests/data_validation/BivariateScatterPlots.py
index c22af160e..af3deb117 100644
--- a/validmind/tests/data_validation/BivariateScatterPlots.py
+++ b/validmind/tests/data_validation/BivariateScatterPlots.py
@@ -3,15 +3,17 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import itertools
+from typing import Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 
 
 @tags("tabular_data", "numerical_data", "visualization")
 @tasks("classification")
-def BivariateScatterPlots(dataset):
+def BivariateScatterPlots(dataset) -> Tuple[go.Figure, RawData]:
     """
     Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables
     in machine learning classification tasks.
diff --git a/validmind/tests/data_validation/BoxPierce.py b/validmind/tests/data_validation/BoxPierce.py
index a85e57a8e..035b2ee8a 100644
--- a/validmind/tests/data_validation/BoxPierce.py
+++ b/validmind/tests/data_validation/BoxPierce.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import pandas as pd
 from statsmodels.stats.diagnostic import acorr_ljungbox
 
@@ -10,7 +13,7 @@
 
 @tasks("regression")
 @tags("time_series_data", "forecasting", "statistical_test", "statsmodels")
-def BoxPierce(dataset):
+def BoxPierce(dataset) -> Tuple[pd.DataFrame, RawData]:
     """
     Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance.
 
diff --git a/validmind/tests/data_validation/ChiSquaredFeaturesTable.py b/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
index 87d553651..2e6620a25 100644
--- a/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
+++ b/validmind/tests/data_validation/ChiSquaredFeaturesTable.py
@@ -12,7 +12,7 @@
 
 @tags("tabular_data", "categorical_data", "statistical_test")
 @tasks("classification")
-def ChiSquaredFeaturesTable(dataset, p_threshold=0.05):
+def ChiSquaredFeaturesTable(dataset, p_threshold=0.05) -> pd.DataFrame:
     """
     Assesses the statistical association between categorical features and a target variable using the Chi-Squared test.
 
diff --git a/validmind/tests/data_validation/ClassImbalance.py b/validmind/tests/data_validation/ClassImbalance.py
index 14d4eacf3..f85a99ea1 100644
--- a/validmind/tests/data_validation/ClassImbalance.py
+++ b/validmind/tests/data_validation/ClassImbalance.py
@@ -20,7 +20,7 @@
 @tasks("classification")
 def ClassImbalance(
     dataset: VMDataset, min_percent_threshold: int = 10
-) -> Tuple[Dict[str, Any], go.Figure, bool]:
+) -> Tuple[Dict[str, Any], go.Figure, bool, RawData]:
     """
     Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model.
 
diff --git a/validmind/tests/data_validation/DatasetDescription.py b/validmind/tests/data_validation/DatasetDescription.py
index 64fe81db7..5b40a46a6 100644
--- a/validmind/tests/data_validation/DatasetDescription.py
+++ b/validmind/tests/data_validation/DatasetDescription.py
@@ -4,6 +4,7 @@
 
 import re
 from collections import Counter
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 
@@ -142,7 +143,9 @@ def describe_column(df, column):
 
 @tags("tabular_data", "time_series_data", "text_data")
 @tasks("classification", "regression", "text_classification", "text_summarization")
-def DatasetDescription(dataset: VMDataset):
+def DatasetDescription(
+    dataset: VMDataset,
+) -> Tuple[Dict[str, List[Dict[str, Any]]], RawData]:
     """
     Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset.
 
diff --git a/validmind/tests/data_validation/DatasetSplit.py b/validmind/tests/data_validation/DatasetSplit.py
index ee6e2b2c2..d4c103c63 100644
--- a/validmind/tests/data_validation/DatasetSplit.py
+++ b/validmind/tests/data_validation/DatasetSplit.py
@@ -2,7 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+
+from typing import Any, Dict, List, Tuple
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
@@ -17,7 +18,7 @@
 
 @tags("tabular_data", "time_series_data", "text_data")
 @tasks("classification", "regression", "text_classification", "text_summarization")
-def DatasetSplit(datasets: List[VMDataset]):
+def DatasetSplit(datasets: List[VMDataset]) -> Tuple[List[Dict[str, Any]], RawData]:
     """
     Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML
     model.
diff --git a/validmind/tests/data_validation/DescriptiveStatistics.py b/validmind/tests/data_validation/DescriptiveStatistics.py
index 3e9e929e4..b64bfe138 100644
--- a/validmind/tests/data_validation/DescriptiveStatistics.py
+++ b/validmind/tests/data_validation/DescriptiveStatistics.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict
+
 import pandas as pd
 
 from validmind import tags, tasks
@@ -46,7 +48,7 @@ def get_summary_statistics_categorical(df, categorical_fields):
 
 @tags("tabular_data", "time_series_data", "data_quality")
 @tasks("classification", "regression")
-def DescriptiveStatistics(dataset: VMDataset):
+def DescriptiveStatistics(dataset: VMDataset) -> Dict[str, Any]:
     """
     Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's
     dataset.
diff --git a/validmind/tests/data_validation/DickeyFullerGLS.py b/validmind/tests/data_validation/DickeyFullerGLS.py
index 0a9dda2c6..efede3b5b 100644
--- a/validmind/tests/data_validation/DickeyFullerGLS.py
+++ b/validmind/tests/data_validation/DickeyFullerGLS.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, Tuple
+
 import pandas as pd
 from arch.unitroot import DFGLS
 from numpy.linalg import LinAlgError
@@ -16,7 +18,7 @@
 
 @tags("time_series_data", "forecasting", "unit_root_test")
 @tasks("regression")
-def DickeyFullerGLS(dataset: VMDataset):
+def DickeyFullerGLS(dataset: VMDataset) -> Tuple[Dict[str, Any], RawData]:
     """
     Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration.
 
diff --git a/validmind/tests/data_validation/Duplicates.py b/validmind/tests/data_validation/Duplicates.py
index 3dc748452..8d2d1f0e3 100644
--- a/validmind/tests/data_validation/Duplicates.py
+++ b/validmind/tests/data_validation/Duplicates.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, Tuple
+
 import pandas as pd
 
 from validmind import tags, tasks
@@ -9,7 +11,7 @@
 
 @tags("tabular_data", "data_quality", "text_data")
 @tasks("classification", "regression")
-def Duplicates(dataset, min_threshold=1):
+def Duplicates(dataset, min_threshold=1) -> Tuple[Dict[str, Any], bool]:
     """
     Tests dataset for duplicate entries, ensuring model reliability via data quality verification.
 
diff --git a/validmind/tests/data_validation/EngleGrangerCoint.py b/validmind/tests/data_validation/EngleGrangerCoint.py
index 0c525f758..e43c6a2b7 100644
--- a/validmind/tests/data_validation/EngleGrangerCoint.py
+++ b/validmind/tests/data_validation/EngleGrangerCoint.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Dict
+
 import pandas as pd
 from statsmodels.tsa.stattools import coint
 
@@ -12,7 +15,9 @@
 
 @tags("time_series_data", "statistical_test", "forecasting")
 @tasks("regression")
-def EngleGrangerCoint(dataset: VMDataset, threshold: float = 0.05):
+def EngleGrangerCoint(
+    dataset: VMDataset, threshold: float = 0.05
+) -> Dict[str, pd.DataFrame]:
     """
     Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test.
 
diff --git a/validmind/tests/data_validation/FeatureTargetCorrelationPlot.py b/validmind/tests/data_validation/FeatureTargetCorrelationPlot.py
index 83c2523db..bef6ed546 100644
--- a/validmind/tests/data_validation/FeatureTargetCorrelationPlot.py
+++ b/validmind/tests/data_validation/FeatureTargetCorrelationPlot.py
@@ -3,6 +3,8 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Tuple
+
 import numpy as np
 import plotly.graph_objects as go
 
@@ -11,7 +13,7 @@
 
 @tags("tabular_data", "visualization", "correlation")
 @tasks("classification", "regression")
-def FeatureTargetCorrelationPlot(dataset, fig_height=600):
+def FeatureTargetCorrelationPlot(dataset, fig_height=600) -> Tuple[go.Figure, RawData]:
     """
     Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar
     plot.
diff --git a/validmind/tests/data_validation/HighCardinality.py b/validmind/tests/data_validation/HighCardinality.py
index 078b7f034..9faa5129d 100644
--- a/validmind/tests/data_validation/HighCardinality.py
+++ b/validmind/tests/data_validation/HighCardinality.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
 
@@ -13,7 +15,7 @@ def HighCardinality(
     num_threshold: int = 100,
     percent_threshold: float = 0.1,
     threshold_type: str = "percent",
-):
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting.
 
diff --git a/validmind/tests/data_validation/HighPearsonCorrelation.py b/validmind/tests/data_validation/HighPearsonCorrelation.py
index d533ca3c1..375662433 100644
--- a/validmind/tests/data_validation/HighPearsonCorrelation.py
+++ b/validmind/tests/data_validation/HighPearsonCorrelation.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
 
@@ -13,7 +16,7 @@ def HighPearsonCorrelation(
     max_threshold: float = 0.3,
     top_n_correlations: int = 10,
     feature_columns: list = None,
-):
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity.
 
diff --git a/validmind/tests/data_validation/IQROutliersBarPlot.py b/validmind/tests/data_validation/IQROutliersBarPlot.py
index c8c639750..7576fbc1b 100644
--- a/validmind/tests/data_validation/IQROutliersBarPlot.py
+++ b/validmind/tests/data_validation/IQROutliersBarPlot.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
@@ -22,7 +25,7 @@ def compute_outliers(series, threshold):
 @tasks("classification", "regression")
 def IQROutliersBarPlot(
     dataset: VMDataset, threshold: float = 1.5, fig_width: int = 800
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method.
 
diff --git a/validmind/tests/data_validation/IQROutliersTable.py b/validmind/tests/data_validation/IQROutliersTable.py
index d9e154ca4..ce802eae3 100644
--- a/validmind/tests/data_validation/IQROutliersTable.py
+++ b/validmind/tests/data_validation/IQROutliersTable.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Any, Dict, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
 
@@ -18,7 +21,9 @@ def compute_outliers(series, threshold=1.5):
 
 @tags("tabular_data", "numerical_data")
 @tasks("classification", "regression")
-def IQROutliersTable(dataset: VMDataset, threshold: float = 1.5):
+def IQROutliersTable(
+    dataset: VMDataset, threshold: float = 1.5
+) -> Tuple[Dict[str, Any], RawData]:
     """
     Determines and summarizes outliers in numerical features using the Interquartile Range method.
 
diff --git a/validmind/tests/data_validation/IsolationForestOutliers.py b/validmind/tests/data_validation/IsolationForestOutliers.py
index 5120a97c9..76475e7d9 100644
--- a/validmind/tests/data_validation/IsolationForestOutliers.py
+++ b/validmind/tests/data_validation/IsolationForestOutliers.py
@@ -3,7 +3,9 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import itertools
+from typing import Tuple
 
+import matplotlib.figure
 import matplotlib.pyplot as plt
 import seaborn as sns
 from sklearn.ensemble import IsolationForest
@@ -19,7 +21,7 @@ def IsolationForestOutliers(
     random_state: int = 0,
     contamination: float = 0.1,
     feature_columns: list = None,
-):
+) -> Tuple[matplotlib.figure.Figure, RawData]:
     """
     Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots.
 
diff --git a/validmind/tests/data_validation/JarqueBera.py b/validmind/tests/data_validation/JarqueBera.py
index d9857f8f5..78888fd9d 100644
--- a/validmind/tests/data_validation/JarqueBera.py
+++ b/validmind/tests/data_validation/JarqueBera.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 from statsmodels.stats.stattools import jarque_bera
 
@@ -10,7 +12,7 @@
 
 @tasks("classification", "regression")
 @tags("tabular_data", "data_distribution", "statistical_test", "statsmodels")
-def JarqueBera(dataset):
+def JarqueBera(dataset) -> Tuple[pd.DataFrame, RawData]:
     """
     Assesses normality of dataset features in an ML model using the Jarque-Bera test.
 
diff --git a/validmind/tests/data_validation/KPSS.py b/validmind/tests/data_validation/KPSS.py
index 9d3d4985e..6dcb0134f 100644
--- a/validmind/tests/data_validation/KPSS.py
+++ b/validmind/tests/data_validation/KPSS.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict
+
 import pandas as pd
 from statsmodels.tsa.stattools import kpss
 
@@ -15,7 +17,7 @@
 
 @tags("time_series_data", "stationarity", "unit_root_test", "statsmodels")
 @tasks("data_validation")
-def KPSS(dataset: VMDataset):
+def KPSS(dataset: VMDataset) -> Dict[str, Any]:
     """
     Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test.
 
diff --git a/validmind/tests/data_validation/LJungBox.py b/validmind/tests/data_validation/LJungBox.py
index 6ffb966d7..7abccf45b 100644
--- a/validmind/tests/data_validation/LJungBox.py
+++ b/validmind/tests/data_validation/LJungBox.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 from statsmodels.stats.diagnostic import acorr_ljungbox
 
@@ -10,7 +12,7 @@
 
 @tasks("regression")
 @tags("time_series_data", "forecasting", "statistical_test", "statsmodels")
-def LJungBox(dataset):
+def LJungBox(dataset) -> Tuple[pd.DataFrame, RawData]:
     """
     Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature.
 
diff --git a/validmind/tests/data_validation/LaggedCorrelationHeatmap.py b/validmind/tests/data_validation/LaggedCorrelationHeatmap.py
index d295b1caa..b1539c02c 100644
--- a/validmind/tests/data_validation/LaggedCorrelationHeatmap.py
+++ b/validmind/tests/data_validation/LaggedCorrelationHeatmap.py
@@ -2,9 +2,12 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.figure_factory as ff
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
@@ -15,7 +18,9 @@
 
 @tags("time_series_data", "visualization")
 @tasks("regression")
-def LaggedCorrelationHeatmap(dataset: VMDataset, num_lags: int = 10):
+def LaggedCorrelationHeatmap(
+    dataset: VMDataset, num_lags: int = 10
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses and visualizes correlation between target variable and lagged independent variables in a time-series
     dataset.
diff --git a/validmind/tests/data_validation/MissingValues.py b/validmind/tests/data_validation/MissingValues.py
index 7aae5a14d..0c8aac33e 100644
--- a/validmind/tests/data_validation/MissingValues.py
+++ b/validmind/tests/data_validation/MissingValues.py
@@ -2,13 +2,17 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset
 
 
 @tags("tabular_data", "data_quality")
 @tasks("classification", "regression")
-def MissingValues(dataset: VMDataset, min_threshold: int = 1):
+def MissingValues(
+    dataset: VMDataset, min_threshold: int = 1
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold.
 
diff --git a/validmind/tests/data_validation/MissingValuesBarPlot.py b/validmind/tests/data_validation/MissingValuesBarPlot.py
index 8ec66519d..dcc5f8b00 100644
--- a/validmind/tests/data_validation/MissingValuesBarPlot.py
+++ b/validmind/tests/data_validation/MissingValuesBarPlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
@@ -12,7 +14,7 @@
 @tasks("classification", "regression")
 def MissingValuesBarPlot(
     dataset: VMDataset, threshold: int = 80, fig_height: int = 600
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on
     identifying high-risk columns based on a user-defined threshold.
diff --git a/validmind/tests/data_validation/MutualInformation.py b/validmind/tests/data_validation/MutualInformation.py
index 148257dd7..d7f3d2e69 100644
--- a/validmind/tests/data_validation/MutualInformation.py
+++ b/validmind/tests/data_validation/MutualInformation.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import plotly.graph_objects as go
 from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
 
@@ -14,7 +17,7 @@
 @tasks("classification", "regression")
 def MutualInformation(
     dataset: VMDataset, min_threshold: float = 0.01, task: str = "classification"
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Calculates mutual information scores between features and target variable to evaluate feature relevance.
 
diff --git a/validmind/tests/data_validation/PearsonCorrelationMatrix.py b/validmind/tests/data_validation/PearsonCorrelationMatrix.py
index 5b8d756a1..ee162534c 100644
--- a/validmind/tests/data_validation/PearsonCorrelationMatrix.py
+++ b/validmind/tests/data_validation/PearsonCorrelationMatrix.py
@@ -3,6 +3,8 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Tuple
+
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,7 @@
 
 @tags("tabular_data", "numerical_data", "correlation")
 @tasks("classification", "regression")
-def PearsonCorrelationMatrix(dataset):
+def PearsonCorrelationMatrix(dataset) -> Tuple[go.Figure, RawData]:
     """
     Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map.
 
diff --git a/validmind/tests/data_validation/PhillipsPerronArch.py b/validmind/tests/data_validation/PhillipsPerronArch.py
index 401ac6125..3be6cd3b8 100644
--- a/validmind/tests/data_validation/PhillipsPerronArch.py
+++ b/validmind/tests/data_validation/PhillipsPerronArch.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict
+
 import numpy as np
 import pandas as pd
 from arch.unitroot import PhillipsPerron
@@ -17,7 +19,7 @@
 
 @tags("time_series_data", "forecasting", "statistical_test", "unit_root_test")
 @tasks("regression")
-def PhillipsPerronArch(dataset: VMDataset):
+def PhillipsPerronArch(dataset: VMDataset) -> Dict[str, Any]:
     """
     Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test.
 
diff --git a/validmind/tests/data_validation/ProtectedClassesCombination.py b/validmind/tests/data_validation/ProtectedClassesCombination.py
index 606144d45..f6e651e87 100644
--- a/validmind/tests/data_validation/ProtectedClassesCombination.py
+++ b/validmind/tests/data_validation/ProtectedClassesCombination.py
@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import sys
+from typing import Dict, Tuple
 
 import pandas as pd
 import plotly.graph_objects as go
@@ -11,6 +12,7 @@
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
 from validmind.logging import get_logger
+from validmind.vm_models import VMDataset, VMModel
 
 try:
     from fairlearn.metrics import (
@@ -33,7 +35,9 @@
 
 @tags("bias_and_fairness")
 @tasks("classification", "regression")
-def ProtectedClassesCombination(dataset, model, protected_classes=None):
+def ProtectedClassesCombination(
+    dataset: VMDataset, model: VMModel, protected_classes=None
+) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], go.Figure, RawData]:
     """
     Visualizes combinations of protected classes and their corresponding error metric differences.
 
diff --git a/validmind/tests/data_validation/ProtectedClassesDescription.py b/validmind/tests/data_validation/ProtectedClassesDescription.py
index cb4e631ac..1912878d3 100644
--- a/validmind/tests/data_validation/ProtectedClassesDescription.py
+++ b/validmind/tests/data_validation/ProtectedClassesDescription.py
@@ -3,6 +3,8 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import List, Tuple, Union
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -14,7 +16,9 @@
 
 @tags("bias_and_fairness", "descriptive_statistics")
 @tasks("classification", "regression")
-def ProtectedClassesDescription(dataset, protected_classes=None):
+def ProtectedClassesDescription(
+    dataset, protected_classes=None
+) -> Tuple[pd.DataFrame, Union[go.Figure, List[go.Figure]], RawData]:
     """
     Visualizes the distribution of protected classes in the dataset relative to the target variable
     and provides descriptive statistics.
diff --git a/validmind/tests/data_validation/ProtectedClassesDisparity.py b/validmind/tests/data_validation/ProtectedClassesDisparity.py
index ac9b88251..1d3a9f0d6 100644
--- a/validmind/tests/data_validation/ProtectedClassesDisparity.py
+++ b/validmind/tests/data_validation/ProtectedClassesDisparity.py
@@ -4,12 +4,14 @@
 
 import io
 import sys
+from typing import Any, List, Tuple
 
 import pandas as pd
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
 from validmind.logging import get_logger
+from validmind.vm_models import VMDataset, VMModel
 
 try:
     import aequitas.plot as ap
@@ -28,12 +30,12 @@
 @tags("bias_and_fairness")
 @tasks("classification", "regression")
 def ProtectedClassesDisparity(
-    dataset,
-    model,
+    dataset: VMDataset,
+    model: VMModel,
     protected_classes=None,
     disparity_tolerance=1.25,
     metrics=["fnr", "fpr", "tpr"],
-):
+) -> Tuple[pd.DataFrame, List[bytes], Any, RawData]:
     """
     Investigates disparities in model performance across different protected class segments.
 
diff --git a/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.py b/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.py
index 31124a621..5783e691c 100644
--- a/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.py
+++ b/validmind/tests/data_validation/ProtectedClassesThresholdOptimizer.py
@@ -4,13 +4,16 @@
 
 import json
 import sys
+from typing import Any, Dict, Tuple
 
+import matplotlib.figure
 import matplotlib.pyplot as plt
 import pandas as pd
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
 from validmind.logging import get_logger
+from validmind.vm_models import VMDataset
 
 try:
     from fairlearn.metrics import (
@@ -35,8 +38,12 @@
 @tags("bias_and_fairness")
 @tasks("classification", "regression")
 def ProtectedClassesThresholdOptimizer(
-    dataset, pipeline=None, protected_classes=None, X_train=None, y_train=None
-):
+    dataset: VMDataset,
+    pipeline=None,
+    protected_classes=None,
+    X_train=None,
+    y_train=None,
+) -> Tuple[Dict[str, Any], matplotlib.figure.Figure, RawData]:
     """
     Obtains a classifier by applying group-specific thresholds to the provided estimator.
 
diff --git a/validmind/tests/data_validation/RollingStatsPlot.py b/validmind/tests/data_validation/RollingStatsPlot.py
index 7f05735f6..34d73291a 100644
--- a/validmind/tests/data_validation/RollingStatsPlot.py
+++ b/validmind/tests/data_validation/RollingStatsPlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import matplotlib.pyplot as plt
 import pandas as pd
 
@@ -42,7 +44,9 @@ def plot_rolling_statistics(df, col, window_size):
 
 @tags("time_series_data", "visualization", "stationarity")
 @tasks("regression")
-def RollingStatsPlot(dataset: VMDataset, window_size: int = 12):
+def RollingStatsPlot(
+    dataset: VMDataset, window_size: int = 12
+) -> Tuple[plt.Figure, RawData]:
     """
     Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified
     window.
diff --git a/validmind/tests/data_validation/RunsTest.py b/validmind/tests/data_validation/RunsTest.py
index 7004b238d..227b49b04 100644
--- a/validmind/tests/data_validation/RunsTest.py
+++ b/validmind/tests/data_validation/RunsTest.py
@@ -10,7 +10,7 @@
 
 @tasks("classification", "regression")
 @tags("tabular_data", "statistical_test", "statsmodels")
-def RunsTest(dataset):
+def RunsTest(dataset) -> pd.DataFrame:
     """
     Executes Runs Test on ML model to detect non-random patterns in output data sequence.
 
diff --git a/validmind/tests/data_validation/ScatterPlot.py b/validmind/tests/data_validation/ScatterPlot.py
index a7e037475..899ef8f5d 100644
--- a/validmind/tests/data_validation/ScatterPlot.py
+++ b/validmind/tests/data_validation/ScatterPlot.py
@@ -2,6 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+import matplotlib.figure
 import matplotlib.pyplot as plt
 import seaborn as sns
 
@@ -10,7 +11,7 @@
 
 @tags("tabular_data", "visualization")
 @tasks("classification", "regression")
-def ScatterPlot(dataset):
+def ScatterPlot(dataset) -> matplotlib.figure.Figure:
     """
     Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices.
 
diff --git a/validmind/tests/data_validation/ScoreBandDefaultRates.py b/validmind/tests/data_validation/ScoreBandDefaultRates.py
index ba995ad1c..5bab7303e 100644
--- a/validmind/tests/data_validation/ScoreBandDefaultRates.py
+++ b/validmind/tests/data_validation/ScoreBandDefaultRates.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 
@@ -16,7 +18,7 @@ def ScoreBandDefaultRates(
     model: VMModel,
     score_column: str = "score",
     score_bands: list = None,
-):
+) -> Tuple[pd.DataFrame, RawData]:
     """
     Analyzes default rates and population distribution across credit score bands.
 
diff --git a/validmind/tests/data_validation/SeasonalDecompose.py b/validmind/tests/data_validation/SeasonalDecompose.py
index e72879a5e..bc0bc3f26 100644
--- a/validmind/tests/data_validation/SeasonalDecompose.py
+++ b/validmind/tests/data_validation/SeasonalDecompose.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
@@ -19,7 +22,9 @@
 
 @tags("time_series_data", "seasonality", "statsmodels")
 @tasks("regression")
-def SeasonalDecompose(dataset: VMDataset, seasonal_model: str = "additive"):
+def SeasonalDecompose(
+    dataset: VMDataset, seasonal_model: str = "additive"
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components.
 
diff --git a/validmind/tests/data_validation/ShapiroWilk.py b/validmind/tests/data_validation/ShapiroWilk.py
index b299040c7..909d097c0 100644
--- a/validmind/tests/data_validation/ShapiroWilk.py
+++ b/validmind/tests/data_validation/ShapiroWilk.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import pandas as pd
 from scipy import stats
 
@@ -10,7 +13,7 @@
 
 @tasks("classification", "regression")
 @tags("tabular_data", "data_distribution", "statistical_test")
-def ShapiroWilk(dataset):
+def ShapiroWilk(dataset) -> Tuple[pd.DataFrame, RawData]:
     """
     Evaluates feature-wise normality of training data using the Shapiro-Wilk test.
 
diff --git a/validmind/tests/data_validation/Skewness.py b/validmind/tests/data_validation/Skewness.py
index 2c7550f75..b255393ec 100644
--- a/validmind/tests/data_validation/Skewness.py
+++ b/validmind/tests/data_validation/Skewness.py
@@ -2,13 +2,15 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import tags, tasks
 from validmind.utils import infer_datatypes
 
 
 @tags("data_quality", "tabular_data")
 @tasks("classification", "regression")
-def Skewness(dataset, max_threshold=1):
+def Skewness(dataset, max_threshold=1) -> Tuple[Dict[str, List[Dict[str, Any]]], bool]:
     """
     Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data
     quality and optimize model performance.
diff --git a/validmind/tests/data_validation/SpreadPlot.py b/validmind/tests/data_validation/SpreadPlot.py
index d4f40df06..a43e0a507 100644
--- a/validmind/tests/data_validation/SpreadPlot.py
+++ b/validmind/tests/data_validation/SpreadPlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
@@ -13,7 +15,7 @@
 
 @tags("time_series_data", "visualization")
 @tasks("regression")
-def SpreadPlot(dataset: VMDataset):
+def SpreadPlot(dataset: VMDataset) -> Tuple[plt.Figure, RawData]:
     """
     Assesses potential correlations between pairs of time series variables through visualization to enhance
     understanding of their relationships.
diff --git a/validmind/tests/data_validation/TabularCategoricalBarPlots.py b/validmind/tests/data_validation/TabularCategoricalBarPlots.py
index 4a2419f34..d52257b73 100644
--- a/validmind/tests/data_validation/TabularCategoricalBarPlots.py
+++ b/validmind/tests/data_validation/TabularCategoricalBarPlots.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import plotly.graph_objs as go
 
 from validmind import RawData, tags, tasks
@@ -11,7 +14,7 @@
 
 @tags("tabular_data", "visualization")
 @tasks("classification", "regression")
-def TabularCategoricalBarPlots(dataset: VMDataset):
+def TabularCategoricalBarPlots(dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition.
 
diff --git a/validmind/tests/data_validation/TabularDateTimeHistograms.py b/validmind/tests/data_validation/TabularDateTimeHistograms.py
index 9d2a6ffca..b4774e889 100644
--- a/validmind/tests/data_validation/TabularDateTimeHistograms.py
+++ b/validmind/tests/data_validation/TabularDateTimeHistograms.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -12,7 +14,7 @@
 
 @tags("time_series_data", "visualization")
 @tasks("classification", "regression")
-def TabularDateTimeHistograms(dataset: VMDataset):
+def TabularDateTimeHistograms(dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime
     data.
diff --git a/validmind/tests/data_validation/TabularDescriptionTables.py b/validmind/tests/data_validation/TabularDescriptionTables.py
index f568545e4..266bf1bf9 100644
--- a/validmind/tests/data_validation/TabularDescriptionTables.py
+++ b/validmind/tests/data_validation/TabularDescriptionTables.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import pandas as pd
 
 from validmind import tags, tasks
@@ -9,7 +12,7 @@
 
 @tags("tabular_data")
 @tasks("classification", "regression")
-def TabularDescriptionTables(dataset):
+def TabularDescriptionTables(dataset) -> Tuple[pd.DataFrame]:
     """
     Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset.
 
diff --git a/validmind/tests/data_validation/TabularNumericalHistograms.py b/validmind/tests/data_validation/TabularNumericalHistograms.py
index 438cdfd89..2121a9ed6 100644
--- a/validmind/tests/data_validation/TabularNumericalHistograms.py
+++ b/validmind/tests/data_validation/TabularNumericalHistograms.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.graph_objs as go
 
 from validmind import tags, tasks
@@ -10,7 +12,7 @@
 
 @tags("tabular_data", "visualization")
 @tasks("classification", "regression")
-def TabularNumericalHistograms(dataset: VMDataset):
+def TabularNumericalHistograms(dataset: VMDataset) -> Tuple[go.Figure]:
     """
     Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and
     detect potential issues.
diff --git a/validmind/tests/data_validation/TargetRateBarPlots.py b/validmind/tests/data_validation/TargetRateBarPlots.py
index 094906fd7..5b860dc3d 100644
--- a/validmind/tests/data_validation/TargetRateBarPlots.py
+++ b/validmind/tests/data_validation/TargetRateBarPlots.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import numpy as np
 import plotly.graph_objs as go
 from plotly.subplots import make_subplots
@@ -13,7 +16,7 @@
 
 @tags("tabular_data", "visualization", "categorical_data")
 @tasks("classification")
-def TargetRateBarPlots(dataset: VMDataset):
+def TargetRateBarPlots(dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Generates bar plots visualizing the default rates of categorical features for a classification machine learning
     model.
diff --git a/validmind/tests/data_validation/TimeSeriesDescription.py b/validmind/tests/data_validation/TimeSeriesDescription.py
index ad2fd08a1..0dc5e2c2d 100644
--- a/validmind/tests/data_validation/TimeSeriesDescription.py
+++ b/validmind/tests/data_validation/TimeSeriesDescription.py
@@ -9,7 +9,7 @@
 
 @tags("time_series_data", "analysis")
 @tasks("regression")
-def TimeSeriesDescription(dataset):
+def TimeSeriesDescription(dataset) -> pd.DataFrame:
     """
     Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,
     patterns, and data quality issues.
diff --git a/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py b/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
index b91f279dd..75d15eb87 100644
--- a/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
+++ b/validmind/tests/data_validation/TimeSeriesDescriptiveStatistics.py
@@ -10,7 +10,7 @@
 
 @tags("time_series_data", "analysis")
 @tasks("regression")
-def TimeSeriesDescriptiveStatistics(dataset):
+def TimeSeriesDescriptiveStatistics(dataset) -> pd.DataFrame:
     """
     Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues.
 
diff --git a/validmind/tests/data_validation/TimeSeriesFrequency.py b/validmind/tests/data_validation/TimeSeriesFrequency.py
index f89f96e09..5c26ef82a 100644
--- a/validmind/tests/data_validation/TimeSeriesFrequency.py
+++ b/validmind/tests/data_validation/TimeSeriesFrequency.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -12,7 +14,9 @@
 
 @tags("time_series_data")
 @tasks("regression")
-def TimeSeriesFrequency(dataset: VMDataset):
+def TimeSeriesFrequency(
+    dataset: VMDataset,
+) -> Tuple[List[Dict[str, Any]], go.Figure, bool, RawData]:
     """
     Evaluates consistency of time series data frequency and generates a frequency plot.
 
diff --git a/validmind/tests/data_validation/TimeSeriesHistogram.py b/validmind/tests/data_validation/TimeSeriesHistogram.py
index fd38e1eee..7a304357c 100644
--- a/validmind/tests/data_validation/TimeSeriesHistogram.py
+++ b/validmind/tests/data_validation/TimeSeriesHistogram.py
@@ -2,8 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 
 from validmind import tags, tasks
 from validmind.logging import get_logger
@@ -13,7 +16,7 @@
 
 @tags("data_validation", "visualization", "time_series_data")
 @tasks("regression", "time_series_forecasting")
-def TimeSeriesHistogram(dataset, nbins=30):
+def TimeSeriesHistogram(dataset, nbins=30) -> Tuple[go.Figure]:
     """
     Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines.
 
diff --git a/validmind/tests/data_validation/TimeSeriesLinePlot.py b/validmind/tests/data_validation/TimeSeriesLinePlot.py
index 4df6f1472..16f30d6ac 100644
--- a/validmind/tests/data_validation/TimeSeriesLinePlot.py
+++ b/validmind/tests/data_validation/TimeSeriesLinePlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -12,7 +14,7 @@
 
 @tags("time_series_data", "visualization")
 @tasks("regression")
-def TimeSeriesLinePlot(dataset: VMDataset):
+def TimeSeriesLinePlot(dataset: VMDataset) -> Tuple[go.Figure]:
     """
     Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time.
 
diff --git a/validmind/tests/data_validation/TimeSeriesMissingValues.py b/validmind/tests/data_validation/TimeSeriesMissingValues.py
index be7199387..b12a41bf2 100644
--- a/validmind/tests/data_validation/TimeSeriesMissingValues.py
+++ b/validmind/tests/data_validation/TimeSeriesMissingValues.py
@@ -2,9 +2,12 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 import pandas as pd
 import plotly.express as px
 import plotly.figure_factory as ff
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.errors import SkipTestError
@@ -13,7 +16,9 @@
 
 @tags("time_series_data")
 @tasks("regression")
-def TimeSeriesMissingValues(dataset: VMDataset, min_threshold: int = 1):
+def TimeSeriesMissingValues(
+    dataset: VMDataset, min_threshold: int = 1
+) -> Tuple[List[Dict[str, Any]], go.Figure, go.Figure, bool, RawData]:
     """
     Validates time-series data quality by confirming the count of missing values is below a certain threshold.
 
diff --git a/validmind/tests/data_validation/TimeSeriesOutliers.py b/validmind/tests/data_validation/TimeSeriesOutliers.py
index 6cf926e62..6718c4a18 100644
--- a/validmind/tests/data_validation/TimeSeriesOutliers.py
+++ b/validmind/tests/data_validation/TimeSeriesOutliers.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List, Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -12,7 +14,9 @@
 
 @tags("time_series_data")
 @tasks("regression")
-def TimeSeriesOutliers(dataset: VMDataset, zscore_threshold: int = 3):
+def TimeSeriesOutliers(
+    dataset: VMDataset, zscore_threshold: int = 3
+) -> Tuple[pd.DataFrame, List[go.Figure], bool, RawData]:
     """
     Identifies and visualizes outliers in time-series data using the z-score method.
 
diff --git a/validmind/tests/data_validation/TooManyZeroValues.py b/validmind/tests/data_validation/TooManyZeroValues.py
index 539f3f424..adc04381d 100644
--- a/validmind/tests/data_validation/TooManyZeroValues.py
+++ b/validmind/tests/data_validation/TooManyZeroValues.py
@@ -2,13 +2,18 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Any, Dict, List, Tuple
+
 from validmind.tests import tags, tasks
 from validmind.vm_models import VMDataset
 
 
 @tags("tabular_data")
 @tasks("regression", "classification")
-def TooManyZeroValues(dataset: VMDataset, max_percent_threshold: float = 0.03):
+def TooManyZeroValues(
+    dataset: VMDataset, max_percent_threshold: float = 0.03
+) -> Tuple[List[Dict[str, Any]], bool]:
     """
     Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold
     percentage.
diff --git a/validmind/tests/data_validation/UniqueRows.py b/validmind/tests/data_validation/UniqueRows.py
index e6e0278bf..ac60ff0f3 100644
--- a/validmind/tests/data_validation/UniqueRows.py
+++ b/validmind/tests/data_validation/UniqueRows.py
@@ -2,13 +2,17 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import tags, tasks
 from validmind.vm_models import VMDataset
 
 
 @tags("tabular_data")
 @tasks("regression", "classification")
-def UniqueRows(dataset: VMDataset, min_percent_threshold: float = 1):
+def UniqueRows(
+    dataset: VMDataset, min_percent_threshold: float = 1
+) -> Tuple[List[Dict[str, Any]], bool]:
     """
     Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold.
 
diff --git a/validmind/tests/data_validation/WOEBinPlots.py b/validmind/tests/data_validation/WOEBinPlots.py
index 3a72a4ddc..ff974bdf9 100644
--- a/validmind/tests/data_validation/WOEBinPlots.py
+++ b/validmind/tests/data_validation/WOEBinPlots.py
@@ -2,6 +2,9 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.express as px
@@ -24,7 +27,7 @@ def WOEBinPlots(
     breaks_adj: list = None,
     fig_height: int = 600,
     fig_width: int = 500,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power
     of categorical variables in a data set.
diff --git a/validmind/tests/data_validation/WOEBinTable.py b/validmind/tests/data_validation/WOEBinTable.py
index b3ef04098..12726c00d 100644
--- a/validmind/tests/data_validation/WOEBinTable.py
+++ b/validmind/tests/data_validation/WOEBinTable.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, Tuple
+
 import pandas as pd
 import scorecardpy as sc
 
@@ -12,7 +14,9 @@
 
 @tags("tabular_data", "categorical_data")
 @tasks("classification")
-def WOEBinTable(dataset: VMDataset, breaks_adj: list = None):
+def WOEBinTable(
+    dataset: VMDataset, breaks_adj: list = None
+) -> Tuple[Dict[str, pd.DataFrame], RawData]:
     """
     Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power
     in a binary classification model.
diff --git a/validmind/tests/data_validation/ZivotAndrewsArch.py b/validmind/tests/data_validation/ZivotAndrewsArch.py
index 07b689acc..34071e889 100644
--- a/validmind/tests/data_validation/ZivotAndrewsArch.py
+++ b/validmind/tests/data_validation/ZivotAndrewsArch.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, Tuple
+
 import pandas as pd
 from arch.unitroot import ZivotAndrews
 from numpy.linalg import LinAlgError
@@ -16,7 +18,7 @@
 
 @tags("time_series_data", "stationarity", "unit_root_test")
 @tasks("regression")
-def ZivotAndrewsArch(dataset: VMDataset):
+def ZivotAndrewsArch(dataset: VMDataset) -> Tuple[Dict[str, Any], RawData]:
     """
     Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test.
 
diff --git a/validmind/tests/data_validation/nlp/CommonWords.py b/validmind/tests/data_validation/nlp/CommonWords.py
index 52fe819f8..be9e50e0e 100644
--- a/validmind/tests/data_validation/nlp/CommonWords.py
+++ b/validmind/tests/data_validation/nlp/CommonWords.py
@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 from collections import Counter
+from typing import Tuple
 
 import nltk
 import plotly.graph_objects as go
@@ -14,7 +15,7 @@
 
 @tags("nlp", "text_data", "visualization", "frequency_analysis")
 @tasks("text_classification", "text_summarization")
-def CommonWords(dataset: VMDataset):
+def CommonWords(dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns.
 
diff --git a/validmind/tests/data_validation/nlp/Hashtags.py b/validmind/tests/data_validation/nlp/Hashtags.py
index 54d4d6c64..3e49462ef 100644
--- a/validmind/tests/data_validation/nlp/Hashtags.py
+++ b/validmind/tests/data_validation/nlp/Hashtags.py
@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import re
+from typing import Tuple
 
 import plotly.graph_objects as go
 
@@ -13,7 +14,7 @@
 
 @tags("nlp", "text_data", "visualization", "frequency_analysis")
 @tasks("text_classification", "text_summarization")
-def Hashtags(dataset: VMDataset, top_hashtags: int = 25):
+def Hashtags(dataset: VMDataset, top_hashtags: int = 25) -> Tuple[go.Figure, RawData]:
     """
     Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam.
 
diff --git a/validmind/tests/data_validation/nlp/LanguageDetection.py b/validmind/tests/data_validation/nlp/LanguageDetection.py
index 9b6608240..ffab229bc 100644
--- a/validmind/tests/data_validation/nlp/LanguageDetection.py
+++ b/validmind/tests/data_validation/nlp/LanguageDetection.py
@@ -2,7 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.express as px
+import plotly.graph_objects as go
 from langdetect import LangDetectException, detect
 
 from validmind import RawData, tags, tasks
@@ -10,7 +13,7 @@
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def LanguageDetection(dataset):
+def LanguageDetection(dataset) -> Tuple[go.Figure, RawData]:
     """
     Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages.
 
diff --git a/validmind/tests/data_validation/nlp/Mentions.py b/validmind/tests/data_validation/nlp/Mentions.py
index 9d69a05fc..18cdf8a8f 100644
--- a/validmind/tests/data_validation/nlp/Mentions.py
+++ b/validmind/tests/data_validation/nlp/Mentions.py
@@ -3,9 +3,11 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import re
+from typing import Tuple
 
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.errors import SkipTestError
@@ -14,7 +16,7 @@
 
 @tags("nlp", "text_data", "visualization", "frequency_analysis")
 @tasks("text_classification", "text_summarization")
-def Mentions(dataset: VMDataset, top_mentions: int = 25):
+def Mentions(dataset: VMDataset, top_mentions: int = 25) -> Tuple[go.Figure, RawData]:
     """
     Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis.
 
diff --git a/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py b/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
index da4f816f6..11ef05936 100644
--- a/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
+++ b/validmind/tests/data_validation/nlp/PolarityAndSubjectivity.py
@@ -3,8 +3,11 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Dict, Tuple
+
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from textblob import TextBlob
 
 from validmind import RawData, tags, tasks
@@ -12,7 +15,9 @@
 
 @tags("nlp", "text_data", "data_validation")
 @tasks("nlp")
-def PolarityAndSubjectivity(dataset, threshold_subjectivity=0.5, threshold_polarity=0):
+def PolarityAndSubjectivity(
+    dataset, threshold_subjectivity=0.5, threshold_polarity=0
+) -> Tuple[go.Figure, Dict[str, pd.DataFrame], RawData]:
     """
     Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution.
 
diff --git a/validmind/tests/data_validation/nlp/Punctuations.py b/validmind/tests/data_validation/nlp/Punctuations.py
index 352b28a0e..392c107f8 100644
--- a/validmind/tests/data_validation/nlp/Punctuations.py
+++ b/validmind/tests/data_validation/nlp/Punctuations.py
@@ -8,6 +8,7 @@
 
 import string
 from collections import defaultdict
+from typing import Tuple
 
 import plotly.graph_objects as go
 
@@ -16,7 +17,7 @@
 
 @tags("nlp", "text_data", "visualization", "frequency_analysis")
 @tasks("text_classification", "text_summarization", "nlp")
-def Punctuations(dataset, count_mode="token"):
+def Punctuations(dataset, count_mode="token") -> Tuple[go.Figure, RawData]:
     """
     Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset.
 
diff --git a/validmind/tests/data_validation/nlp/Sentiment.py b/validmind/tests/data_validation/nlp/Sentiment.py
index 1c8ef1932..8df9b88a4 100644
--- a/validmind/tests/data_validation/nlp/Sentiment.py
+++ b/validmind/tests/data_validation/nlp/Sentiment.py
@@ -3,6 +3,8 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Tuple
+
 import matplotlib.pyplot as plt
 import nltk
 import seaborn as sns
@@ -13,7 +15,7 @@
 
 @tags("nlp", "text_data", "data_validation")
 @tasks("nlp")
-def Sentiment(dataset):
+def Sentiment(dataset) -> Tuple[plt.Figure, RawData]:
     """
     Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool.
 
diff --git a/validmind/tests/data_validation/nlp/StopWords.py b/validmind/tests/data_validation/nlp/StopWords.py
index ac3236294..789ef79e5 100644
--- a/validmind/tests/data_validation/nlp/StopWords.py
+++ b/validmind/tests/data_validation/nlp/StopWords.py
@@ -7,6 +7,7 @@
 """
 
 from collections import defaultdict
+from typing import Dict, Tuple
 
 import nltk
 import pandas as pd
@@ -21,7 +22,7 @@
 @tasks("text_classification", "text_summarization")
 def StopWords(
     dataset: VMDataset, min_percent_threshold: float = 0.5, num_words: int = 25
-):
+) -> Tuple[Dict[str, pd.DataFrame], go.Figure, bool, RawData]:
     """
     Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold.
 
diff --git a/validmind/tests/data_validation/nlp/TextDescription.py b/validmind/tests/data_validation/nlp/TextDescription.py
index 69f3c17eb..e6cde5c7d 100644
--- a/validmind/tests/data_validation/nlp/TextDescription.py
+++ b/validmind/tests/data_validation/nlp/TextDescription.py
@@ -3,10 +3,12 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import string
+from typing import Tuple
 
 import nltk
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from nltk.corpus import stopwords
 
 from validmind import RawData, tags, tasks
@@ -94,7 +96,7 @@ def TextDescription(
         "``",
     },
     lang: str = "english",
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate
     visualizations.
diff --git a/validmind/tests/data_validation/nlp/Toxicity.py b/validmind/tests/data_validation/nlp/Toxicity.py
index cae2a2ff3..b6ad32beb 100644
--- a/validmind/tests/data_validation/nlp/Toxicity.py
+++ b/validmind/tests/data_validation/nlp/Toxicity.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -11,7 +13,7 @@
 
 @tags("nlp", "text_data", "data_validation")
 @tasks("nlp")
-def Toxicity(dataset):
+def Toxicity(dataset) -> Tuple[plt.Figure, RawData]:
     """
     Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores.
 
diff --git a/validmind/tests/load.py b/validmind/tests/load.py
index 697c64219..f1b1e7b84 100644
--- a/validmind/tests/load.py
+++ b/validmind/tests/load.py
@@ -7,7 +7,17 @@
 import inspect
 import json
 from pprint import pformat
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    Union,
+    get_args,
+    get_origin,
+)
 from uuid import uuid4
 
 import pandas as pd
@@ -18,12 +28,31 @@
 from ..logging import get_logger
 from ..utils import display, format_dataframe, fuzzy_match, md_to_html, test_id_to_name
 from ..vm_models import VMDataset, VMModel
+from ..vm_models.figure import Figure
+from ..vm_models.result import ResultTable
 from .__types__ import TestID
 from ._store import test_provider_store, test_store
 
 logger = get_logger(__name__)
 
 
+try:
+    from matplotlib.figure import Figure as MatplotlibFigure
+except ImportError:
+    MatplotlibFigure = None
+
+try:
+    from plotly.graph_objects import Figure as PlotlyFigure
+except ImportError:
+    PlotlyFigure = None
+
+FIGURE_TYPES = tuple(
+    item for item in (Figure, MatplotlibFigure, PlotlyFigure) if inspect.isclass(item)
+)
+TABLE_TYPES = (pd.DataFrame, ResultTable)
+GENERIC_TABLE_TYPES = (list, dict)
+
+
 INPUT_TYPE_MAP = {
     "dataset": VMDataset,
     "datasets": List[VMDataset],
@@ -32,6 +61,45 @@
 }
 
 
+def _inspect_return_type(annotation: Any) -> Tuple[bool, bool]:
+    """
+    Inspects a return type annotation to determine if it contains a Figure or Table.
+
+    Returns a tuple (has_figure, has_table).
+    """
+    has_figure = False
+    has_table = False
+
+    origin = get_origin(annotation)
+    args = get_args(annotation)
+
+    # A Union means the return type could be one of several types.
+    # A tuple in a type hint means multiple return values.
+    # We recursively inspect the arguments of Union and tuple.
+    if origin is Union or origin is tuple:
+        for arg in args:
+            fig, table = _inspect_return_type(arg)
+            has_figure |= fig
+            has_table |= table
+        return has_figure, has_table
+
+    check_type = origin if origin is not None else annotation
+
+    if not inspect.isclass(check_type):
+        return has_figure, has_table  # Can't do issubclass on non-class like Any
+
+    if FIGURE_TYPES and issubclass(check_type, FIGURE_TYPES):
+        has_figure = True
+
+    if TABLE_TYPES and issubclass(check_type, TABLE_TYPES):
+        has_table = True
+
+    if check_type in GENERIC_TABLE_TYPES:
+        has_table = True
+
+    return has_figure, has_table
+
+
 def _inspect_signature(
     test_func: Callable[..., Any],
 ) -> Tuple[Dict[str, Dict[str, Any]], Dict[str, Dict[str, Any]]]:
@@ -173,23 +241,29 @@ def _pretty_list_tests(
     tests: Dict[str, Callable[..., Any]], truncate: bool = True
 ) -> None:
     """Pretty print a list of tests"""
-    table = [
-        {
-            "ID": test_id,
-            "Name": test_id_to_name(test_id),
-            "Description": _test_description(
-                inspect.getdoc(test),
-                num_lines=(5 if truncate else 999999),
-            ),
-            "Required Inputs": list(test.inputs.keys()),
-            "Params": test.params,
-            "Tags": test.__tags__,
-            "Tasks": test.__tasks__,
-        }
-        for test_id, test in tests.items()
-    ]
+    rows = []
+    for test_id, test in tests.items():
+        has_figure, has_table = _inspect_return_type(
+            inspect.signature(test).return_annotation
+        )
+        rows.append(
+            {
+                "ID": test_id,
+                "Name": test_id_to_name(test_id),
+                "Description": _test_description(
+                    inspect.getdoc(test),
+                    num_lines=(5 if truncate else 999999),
+                ),
+                "Has Figure": has_figure,
+                "Has Table": has_table,
+                "Required Inputs": list(test.inputs.keys()),
+                "Params": test.params,
+                "Tags": test.__tags__,
+                "Tasks": test.__tasks__,
+            }
+        )
 
-    return format_dataframe(pd.DataFrame(table))
+    return format_dataframe(pd.DataFrame(rows))
 
 
 def list_tags() -> List[str]:
diff --git a/validmind/tests/model_validation/BertScore.py b/validmind/tests/model_validation/BertScore.py
index 72f04243d..b26184016 100644
--- a/validmind/tests/model_validation/BertScore.py
+++ b/validmind/tests/model_validation/BertScore.py
@@ -2,21 +2,24 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.tests.utils import validate_prediction
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
 def BertScore(
-    dataset,
-    model,
+    dataset: VMDataset,
+    model: VMModel,
     evaluation_model="distilbert-base-uncased",
-):
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms
     and bar charts, alongside compiling a comprehensive table of descriptive statistics.
diff --git a/validmind/tests/model_validation/BleuScore.py b/validmind/tests/model_validation/BleuScore.py
index 8d32fe0c1..5b51795ee 100644
--- a/validmind/tests/model_validation/BleuScore.py
+++ b/validmind/tests/model_validation/BleuScore.py
@@ -2,17 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.tests.utils import validate_prediction
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def BleuScore(dataset, model):
+def BleuScore(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms
     and bar charts, alongside compiling a comprehensive table of descriptive statistics for BLEU scores.
diff --git a/validmind/tests/model_validation/ClusterSizeDistribution.py b/validmind/tests/model_validation/ClusterSizeDistribution.py
index 4cddbe40f..7bb1443a4 100644
--- a/validmind/tests/model_validation/ClusterSizeDistribution.py
+++ b/validmind/tests/model_validation/ClusterSizeDistribution.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -11,7 +13,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("clustering")
-def ClusterSizeDistribution(dataset: VMDataset, model: VMModel):
+def ClusterSizeDistribution(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions
     with the actual data.
diff --git a/validmind/tests/model_validation/ContextualRecall.py b/validmind/tests/model_validation/ContextualRecall.py
index 5930e9f63..9ce407ecc 100644
--- a/validmind/tests/model_validation/ContextualRecall.py
+++ b/validmind/tests/model_validation/ContextualRecall.py
@@ -2,17 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import nltk
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.tests.utils import validate_prediction
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def ContextualRecall(dataset, model):
+def ContextualRecall(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct
     text, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of
diff --git a/validmind/tests/model_validation/FeaturesAUC.py b/validmind/tests/model_validation/FeaturesAUC.py
index 44c218c8f..403c7a938 100644
--- a/validmind/tests/model_validation/FeaturesAUC.py
+++ b/validmind/tests/model_validation/FeaturesAUC.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
@@ -17,7 +19,9 @@
 
 @tags("feature_importance", "AUC", "visualization")
 @tasks("classification")
-def FeaturesAUC(dataset: VMDataset, fontsize: int = 12, figure_height: int = 500):
+def FeaturesAUC(
+    dataset: VMDataset, fontsize: int = 12, figure_height: int = 500
+) -> Tuple[go.Figure, RawData]:
     """
     Evaluates the discriminatory power of each individual feature within a binary classification model by calculating
     the Area Under the Curve (AUC) for each feature separately.
diff --git a/validmind/tests/model_validation/MeteorScore.py b/validmind/tests/model_validation/MeteorScore.py
index 10ed5b7fb..bc735fc15 100644
--- a/validmind/tests/model_validation/MeteorScore.py
+++ b/validmind/tests/model_validation/MeteorScore.py
@@ -2,17 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.tests.utils import validate_prediction
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def MeteorScore(dataset, model):
+def MeteorScore(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses the quality of machine-generated translations by comparing them to human-produced references using the
     METEOR score, which evaluates precision, recall, and word order.
diff --git a/validmind/tests/model_validation/ModelMetadata.py b/validmind/tests/model_validation/ModelMetadata.py
index 5b3914877..11082684c 100644
--- a/validmind/tests/model_validation/ModelMetadata.py
+++ b/validmind/tests/model_validation/ModelMetadata.py
@@ -6,11 +6,12 @@
 
 from validmind import tags, tasks
 from validmind.utils import get_model_info
+from validmind.vm_models import VMModel
 
 
 @tags("model_training", "metadata")
 @tasks("regression", "time_series_forecasting")
-def ModelMetadata(model):
+def ModelMetadata(model: VMModel) -> pd.DataFrame:
     """
     Compare metadata of different models and generate a summary table with the results.
 
diff --git a/validmind/tests/model_validation/ModelPredictionResiduals.py b/validmind/tests/model_validation/ModelPredictionResiduals.py
index 26a4f837c..e0db2a1e2 100644
--- a/validmind/tests/model_validation/ModelPredictionResiduals.py
+++ b/validmind/tests/model_validation/ModelPredictionResiduals.py
@@ -2,18 +2,26 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Optional, Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 from scipy.stats import kstest
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("residual_analysis", "visualization")
 def ModelPredictionResiduals(
-    dataset, model, nbins=100, p_value_threshold=0.05, start_date=None, end_date=None
-):
+    dataset: VMDataset,
+    model: VMModel,
+    nbins: int = 100,
+    p_value_threshold: float = 0.05,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses normality and behavior of residuals in regression models through visualization and statistical tests.
 
diff --git a/validmind/tests/model_validation/RegardScore.py b/validmind/tests/model_validation/RegardScore.py
index 6a362f09e..119eafe04 100644
--- a/validmind/tests/model_validation/RegardScore.py
+++ b/validmind/tests/model_validation/RegardScore.py
@@ -2,17 +2,23 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.tests.utils import validate_prediction
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def RegardScore(dataset, model):
+def RegardScore(
+    dataset: VMDataset,
+    model: VMModel,
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard
     scores.
diff --git a/validmind/tests/model_validation/RegressionResidualsPlot.py b/validmind/tests/model_validation/RegressionResidualsPlot.py
index 813f6bb62..f8afbf43a 100644
--- a/validmind/tests/model_validation/RegressionResidualsPlot.py
+++ b/validmind/tests/model_validation/RegressionResidualsPlot.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.figure_factory as ff
 import plotly.graph_objects as go
@@ -12,7 +14,9 @@
 
 @tags("model_performance", "visualization")
 @tasks("regression")
-def RegressionResidualsPlot(model: VMModel, dataset: VMDataset, bin_size: float = 0.1):
+def RegressionResidualsPlot(
+    model: VMModel, dataset: VMDataset, bin_size: float = 0.1
+) -> Tuple[go.Figure, go.Figure, RawData]:
     """
     Evaluates regression model performance using residual distribution and actual vs. predicted plots.
 
diff --git a/validmind/tests/model_validation/RougeScore.py b/validmind/tests/model_validation/RougeScore.py
index 82a046143..e83b0f835 100644
--- a/validmind/tests/model_validation/RougeScore.py
+++ b/validmind/tests/model_validation/RougeScore.py
@@ -2,16 +2,23 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 from rouge import Rouge
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def RougeScore(dataset, model, metric="rouge-1"):
+def RougeScore(
+    dataset: VMDataset,
+    model: VMModel,
+    metric: str = "rouge-1",
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide
     comprehensive performance insights.
diff --git a/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py b/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
index 6edaed08f..c5f28b96d 100644
--- a/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
+++ b/validmind/tests/model_validation/TimeSeriesPredictionWithCI.py
@@ -2,17 +2,24 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 from scipy.stats import norm
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("model_predictions", "visualization")
 @tasks("regression", "time_series_forecasting")
-def TimeSeriesPredictionWithCI(dataset, model, confidence=0.95):
+def TimeSeriesPredictionWithCI(
+    dataset: VMDataset,
+    model: VMModel,
+    confidence: float = 0.95,
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence
     intervals.
diff --git a/validmind/tests/model_validation/TimeSeriesPredictionsPlot.py b/validmind/tests/model_validation/TimeSeriesPredictionsPlot.py
index 9a2239fe6..d6260b8c8 100644
--- a/validmind/tests/model_validation/TimeSeriesPredictionsPlot.py
+++ b/validmind/tests/model_validation/TimeSeriesPredictionsPlot.py
@@ -2,14 +2,20 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("model_predictions", "visualization")
 @tasks("regression", "time_series_forecasting")
-def TimeSeriesPredictionsPlot(dataset, model):
+def TimeSeriesPredictionsPlot(
+    dataset: VMDataset,
+    model: VMModel,
+) -> Tuple[go.Figure, RawData]:
     """
     Plot actual vs predicted values for time series data and generate a visual comparison for the model.
 
diff --git a/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.py b/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.py
index 3de28df23..740c5e3cb 100644
--- a/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.py
+++ b/validmind/tests/model_validation/TimeSeriesR2SquareBySegments.py
@@ -2,17 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Optional, Tuple
 
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("model_performance", "sklearn")
 @tasks("regression", "time_series_forecasting")
-def TimeSeriesR2SquareBySegments(dataset, model, segments=None):
+def TimeSeriesR2SquareBySegments(
+    dataset: VMDataset, model: VMModel, segments: Optional[int] = None
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Evaluates the R-Squared values of regression models over specified time segments in time series data to assess
     segment-wise model performance.
diff --git a/validmind/tests/model_validation/TokenDisparity.py b/validmind/tests/model_validation/TokenDisparity.py
index 5d6961a2e..7d9709340 100644
--- a/validmind/tests/model_validation/TokenDisparity.py
+++ b/validmind/tests/model_validation/TokenDisparity.py
@@ -2,15 +2,20 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def TokenDisparity(dataset, model):
+def TokenDisparity(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and
     bar charts, alongside compiling a comprehensive table of descriptive statistics for token counts.
diff --git a/validmind/tests/model_validation/ToxicityScore.py b/validmind/tests/model_validation/ToxicityScore.py
index 37a5a5ce8..62519d522 100644
--- a/validmind/tests/model_validation/ToxicityScore.py
+++ b/validmind/tests/model_validation/ToxicityScore.py
@@ -2,16 +2,21 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import evaluate
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("nlp", "text_data", "visualization")
 @tasks("text_classification", "text_summarization")
-def ToxicityScore(dataset, model):
+def ToxicityScore(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content.
 
diff --git a/validmind/tests/model_validation/embeddings/ClusterDistribution.py b/validmind/tests/model_validation/embeddings/ClusterDistribution.py
index 7c604d527..107a9dc83 100644
--- a/validmind/tests/model_validation/embeddings/ClusterDistribution.py
+++ b/validmind/tests/model_validation/embeddings/ClusterDistribution.py
@@ -2,7 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.cluster import KMeans
 
 from validmind import RawData, tags, tasks
@@ -11,7 +14,9 @@
 
 @tags("llm", "text_data", "embeddings", "visualization")
 @tasks("feature_extraction")
-def ClusterDistribution(model: VMModel, dataset: VMDataset, num_clusters: int = 5):
+def ClusterDistribution(
+    model: VMModel, dataset: VMDataset, num_clusters: int = 5
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering.
 
diff --git a/validmind/tests/model_validation/embeddings/CosineSimilarityComparison.py b/validmind/tests/model_validation/embeddings/CosineSimilarityComparison.py
index 27e919d61..c04cb4ed8 100644
--- a/validmind/tests/model_validation/embeddings/CosineSimilarityComparison.py
+++ b/validmind/tests/model_validation/embeddings/CosineSimilarityComparison.py
@@ -3,18 +3,23 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 from itertools import combinations
+from typing import List, Tuple
 
 import numpy as np
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.metrics.pairwise import cosine_similarity
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
-def CosineSimilarityComparison(dataset, models):
+def CosineSimilarityComparison(
+    dataset: VMDataset, models: List[VMModel]
+) -> Tuple[go.Figure, RawData, pd.DataFrame]:
     """
     Assesses the similarity between embeddings generated by different models using Cosine Similarity, providing both
     statistical and visual insights.
diff --git a/validmind/tests/model_validation/embeddings/CosineSimilarityDistribution.py b/validmind/tests/model_validation/embeddings/CosineSimilarityDistribution.py
index 723a54028..f0e26f1e6 100644
--- a/validmind/tests/model_validation/embeddings/CosineSimilarityDistribution.py
+++ b/validmind/tests/model_validation/embeddings/CosineSimilarityDistribution.py
@@ -2,7 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.metrics.pairwise import cosine_similarity
 
 from validmind import RawData, tags, tasks
@@ -11,7 +14,9 @@
 
 @tags("llm", "text_data", "embeddings", "visualization")
 @tasks("feature_extraction")
-def CosineSimilarityDistribution(dataset: VMDataset, model: VMModel):
+def CosineSimilarityDistribution(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution
     histogram.
diff --git a/validmind/tests/model_validation/embeddings/CosineSimilarityHeatmap.py b/validmind/tests/model_validation/embeddings/CosineSimilarityHeatmap.py
index ae0e8fe8c..383b5c7ec 100644
--- a/validmind/tests/model_validation/embeddings/CosineSimilarityHeatmap.py
+++ b/validmind/tests/model_validation/embeddings/CosineSimilarityHeatmap.py
@@ -2,24 +2,28 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.metrics.pairwise import cosine_similarity
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
 def CosineSimilarityHeatmap(
-    dataset,
-    model,
+    dataset: VMDataset,
+    model: VMModel,
     title="Cosine Similarity Matrix",
     color="Cosine Similarity",
     xaxis_title="Index",
     yaxis_title="Index",
     color_scale="Blues",
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model.
 
diff --git a/validmind/tests/model_validation/embeddings/DescriptiveAnalytics.py b/validmind/tests/model_validation/embeddings/DescriptiveAnalytics.py
index 91a5ec851..ea551955b 100644
--- a/validmind/tests/model_validation/embeddings/DescriptiveAnalytics.py
+++ b/validmind/tests/model_validation/embeddings/DescriptiveAnalytics.py
@@ -2,8 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.express as px
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset, VMModel
@@ -11,7 +14,9 @@
 
 @tags("llm", "text_data", "embeddings", "visualization")
 @tasks("feature_extraction")
-def DescriptiveAnalytics(dataset: VMDataset, model: VMModel):
+def DescriptiveAnalytics(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[go.Figure, go.Figure, go.Figure, RawData]:
     """
     Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation
     histograms.
diff --git a/validmind/tests/model_validation/embeddings/EmbeddingsVisualization2D.py b/validmind/tests/model_validation/embeddings/EmbeddingsVisualization2D.py
index f2a254f45..89dee1bc6 100644
--- a/validmind/tests/model_validation/embeddings/EmbeddingsVisualization2D.py
+++ b/validmind/tests/model_validation/embeddings/EmbeddingsVisualization2D.py
@@ -2,9 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import Union
+from typing import Tuple, Union
 
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.manifold import TSNE
 
 from validmind import RawData, tags, tasks
@@ -17,11 +18,11 @@
 @tags("llm", "text_data", "embeddings", "visualization")
 @tasks("feature_extraction")
 def EmbeddingsVisualization2D(
-    model: VMModel,
     dataset: VMDataset,
+    model: VMModel,
     cluster_column: Union[str, None] = None,
     perplexity: int = 30,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Visualizes 2D representation of text embeddings generated by a model using t-SNE technique.
 
diff --git a/validmind/tests/model_validation/embeddings/EuclideanDistanceComparison.py b/validmind/tests/model_validation/embeddings/EuclideanDistanceComparison.py
index eaca371c7..2d272990d 100644
--- a/validmind/tests/model_validation/embeddings/EuclideanDistanceComparison.py
+++ b/validmind/tests/model_validation/embeddings/EuclideanDistanceComparison.py
@@ -3,18 +3,23 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 from itertools import combinations
+from typing import List, Tuple
 
 import numpy as np
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.metrics.pairwise import euclidean_distances
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
-def EuclideanDistanceComparison(dataset, models):
+def EuclideanDistanceComparison(
+    dataset: VMDataset, models: List[VMModel]
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights
     into model behavior and potential redundancy or diversity.
diff --git a/validmind/tests/model_validation/embeddings/EuclideanDistanceHeatmap.py b/validmind/tests/model_validation/embeddings/EuclideanDistanceHeatmap.py
index da68b0764..49352ed2f 100644
--- a/validmind/tests/model_validation/embeddings/EuclideanDistanceHeatmap.py
+++ b/validmind/tests/model_validation/embeddings/EuclideanDistanceHeatmap.py
@@ -2,24 +2,28 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.metrics.pairwise import euclidean_distances
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
 def EuclideanDistanceHeatmap(
-    dataset,
-    model,
+    dataset: VMDataset,
+    model: VMModel,
     title="Euclidean Distance Matrix",
     color="Euclidean Distance",
     xaxis_title="Index",
     yaxis_title="Index",
     color_scale="Blues",
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model.
 
diff --git a/validmind/tests/model_validation/embeddings/PCAComponentsPairwisePlots.py b/validmind/tests/model_validation/embeddings/PCAComponentsPairwisePlots.py
index a62df5d72..a3cb21813 100644
--- a/validmind/tests/model_validation/embeddings/PCAComponentsPairwisePlots.py
+++ b/validmind/tests/model_validation/embeddings/PCAComponentsPairwisePlots.py
@@ -3,19 +3,24 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import itertools
+from typing import Tuple
 
 import numpy as np
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.decomposition import PCA
 from sklearn.preprocessing import StandardScaler
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
-def PCAComponentsPairwisePlots(dataset, model, n_components=3):
+def PCAComponentsPairwisePlots(
+    dataset: VMDataset, model: VMModel, n_components: int = 3
+) -> Tuple[go.Figure, RawData]:
     """
     Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model
     embeddings.
diff --git a/validmind/tests/model_validation/embeddings/StabilityAnalysisKeyword.py b/validmind/tests/model_validation/embeddings/StabilityAnalysisKeyword.py
index c39e92def..34cc47218 100644
--- a/validmind/tests/model_validation/embeddings/StabilityAnalysisKeyword.py
+++ b/validmind/tests/model_validation/embeddings/StabilityAnalysisKeyword.py
@@ -3,7 +3,10 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import re
-from typing import Dict
+from typing import Dict, Tuple
+
+import pandas as pd
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset, VMModel
@@ -18,7 +21,7 @@ def StabilityAnalysisKeyword(
     model: VMModel,
     keyword_dict: Dict[str, str],
     mean_similarity_threshold: float = 0.7,
-):
+) -> Tuple[go.Figure, pd.DataFrame, RawData]:
     """
     Evaluates robustness of embedding models to keyword swaps in the test dataset.
 
diff --git a/validmind/tests/model_validation/embeddings/StabilityAnalysisRandomNoise.py b/validmind/tests/model_validation/embeddings/StabilityAnalysisRandomNoise.py
index 92e250755..8b3103b0d 100644
--- a/validmind/tests/model_validation/embeddings/StabilityAnalysisRandomNoise.py
+++ b/validmind/tests/model_validation/embeddings/StabilityAnalysisRandomNoise.py
@@ -4,6 +4,10 @@
 
 import random
 import string
+from typing import Tuple
+
+import pandas as pd
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset, VMModel
@@ -69,7 +73,7 @@ def StabilityAnalysisRandomNoise(
     model: VMModel,
     probability: float = 0.02,
     mean_similarity_threshold: float = 0.7,
-):
+) -> Tuple[go.Figure, pd.DataFrame, RawData]:
     """
     Assesses the robustness of text embeddings models to random noise introduced via text perturbations.
 
diff --git a/validmind/tests/model_validation/embeddings/StabilityAnalysisSynonyms.py b/validmind/tests/model_validation/embeddings/StabilityAnalysisSynonyms.py
index a8f14f081..5b4c822a2 100644
--- a/validmind/tests/model_validation/embeddings/StabilityAnalysisSynonyms.py
+++ b/validmind/tests/model_validation/embeddings/StabilityAnalysisSynonyms.py
@@ -3,8 +3,11 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import random
+from typing import Tuple
 
 import nltk
+import pandas as pd
+import plotly.graph_objects as go
 from nltk.corpus import wordnet as wn
 
 from validmind import RawData, tags, tasks
@@ -20,7 +23,7 @@ def StabilityAnalysisSynonyms(
     model: VMModel,
     probability: float = 0.02,
     mean_similarity_threshold: float = 0.7,
-):
+) -> Tuple[go.Figure, pd.DataFrame, RawData]:
     """
     Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly.
 
diff --git a/validmind/tests/model_validation/embeddings/StabilityAnalysisTranslation.py b/validmind/tests/model_validation/embeddings/StabilityAnalysisTranslation.py
index 6dce9a58a..877572a11 100644
--- a/validmind/tests/model_validation/embeddings/StabilityAnalysisTranslation.py
+++ b/validmind/tests/model_validation/embeddings/StabilityAnalysisTranslation.py
@@ -2,6 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
+import pandas as pd
+import plotly.graph_objects as go
 from transformers import MarianMTModel, MarianTokenizer
 
 from validmind import RawData, tags, tasks
@@ -21,7 +25,7 @@ def StabilityAnalysisTranslation(
     source_lang: str = "en",
     target_lang: str = "fr",
     mean_similarity_threshold: float = 0.7,
-):
+) -> Tuple[go.Figure, pd.DataFrame, RawData]:
     """
     Evaluates robustness of text embeddings models to noise introduced by translating the original text to another
     language and back.
diff --git a/validmind/tests/model_validation/embeddings/TSNEComponentsPairwisePlots.py b/validmind/tests/model_validation/embeddings/TSNEComponentsPairwisePlots.py
index 816004a0b..2e81c2637 100644
--- a/validmind/tests/model_validation/embeddings/TSNEComponentsPairwisePlots.py
+++ b/validmind/tests/model_validation/embeddings/TSNEComponentsPairwisePlots.py
@@ -3,25 +3,28 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import itertools
+from typing import Tuple
 
 import numpy as np
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 from sklearn.manifold import TSNE
 from sklearn.preprocessing import StandardScaler
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "dimensionality_reduction", "embeddings")
 @tasks("text_qa", "text_generation", "text_summarization")
 def TSNEComponentsPairwisePlots(
-    dataset,
-    model,
-    n_components=2,
-    perplexity=30,
-    title="t-SNE",
-):
+    dataset: VMDataset,
+    model: VMModel,
+    n_components: int = 2,
+    perplexity: int = 30,
+    title: str = "t-SNE",
+) -> Tuple[go.Figure, RawData]:
     """
     Creates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential
     clustering structures.
diff --git a/validmind/tests/model_validation/ragas/AnswerCorrectness.py b/validmind/tests/model_validation/ragas/AnswerCorrectness.py
index 51936ab41..fa96df436 100644
--- a/validmind/tests/model_validation/ragas/AnswerCorrectness.py
+++ b/validmind/tests/model_validation/ragas/AnswerCorrectness.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm")
 @tasks("text_qa", "text_generation", "text_summarization")
 def AnswerCorrectness(
-    dataset,
-    user_input_column="user_input",
-    response_column="response",
-    reference_column="reference",
+    dataset: VMDataset,
+    user_input_column: str = "user_input",
+    response_column: str = "response",
+    reference_column: str = "reference",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Evaluates the correctness of answers in a dataset with respect to the provided ground
     truths and visualizes the results in a histogram.
diff --git a/validmind/tests/model_validation/ragas/AspectCritic.py b/validmind/tests/model_validation/ragas/AspectCritic.py
index 8eb3e921b..4eba617b5 100644
--- a/validmind/tests/model_validation/ragas/AspectCritic.py
+++ b/validmind/tests/model_validation/ragas/AspectCritic.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, List, Optional, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -39,21 +42,21 @@
 @tags("ragas", "llm", "qualitative")
 @tasks("text_summarization", "text_generation", "text_qa")
 def AspectCritic(
-    dataset,
-    user_input_column="user_input",
-    response_column="response",
-    retrieved_contexts_column=None,
-    aspects: list = [
+    dataset: VMDataset,
+    user_input_column: str = "user_input",
+    response_column: str = "response",
+    retrieved_contexts_column: Optional[str] = None,
+    aspects: List[str] = [
         "coherence",
         "conciseness",
         "correctness",
         "harmfulness",
         "maliciousness",
     ],
-    additional_aspects: list = None,
+    additional_aspects: Optional[List[Tuple[str, str]]] = None,
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, RawData]:
     """
     Evaluates generations against the following aspects: harmfulness, maliciousness,
     coherence, correctness, and conciseness.
diff --git a/validmind/tests/model_validation/ragas/ContextEntityRecall.py b/validmind/tests/model_validation/ragas/ContextEntityRecall.py
index 3f5aa6a35..31c395212 100644
--- a/validmind/tests/model_validation/ragas/ContextEntityRecall.py
+++ b/validmind/tests/model_validation/ragas/ContextEntityRecall.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,12 +33,12 @@
 @tags("ragas", "llm", "retrieval_performance")
 @tasks("text_qa", "text_generation", "text_summarization")
 def ContextEntityRecall(
-    dataset,
+    dataset: VMDataset,
     retrieved_contexts_column: str = "retrieved_contexts",
     reference_column: str = "reference",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Evaluates the context entity recall for dataset entries and visualizes the results.
 
diff --git a/validmind/tests/model_validation/ragas/ContextPrecision.py b/validmind/tests/model_validation/ragas/ContextPrecision.py
index 9eb455f3e..bb7dae2f3 100644
--- a/validmind/tests/model_validation/ragas/ContextPrecision.py
+++ b/validmind/tests/model_validation/ragas/ContextPrecision.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm", "retrieval_performance")
 @tasks("text_qa", "text_generation", "text_summarization", "text_classification")
 def ContextPrecision(
-    dataset,
+    dataset: VMDataset,
     user_input_column: str = "user_input",
     retrieved_contexts_column: str = "retrieved_contexts",
     reference_column: str = "reference",
     judge_llm=None,
     judge_embeddings=None,
-):  # noqa: B950
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Context Precision is a metric that evaluates whether all of the ground-truth
     relevant items present in the contexts are ranked higher or not. Ideally all the
diff --git a/validmind/tests/model_validation/ragas/ContextPrecisionWithoutReference.py b/validmind/tests/model_validation/ragas/ContextPrecisionWithoutReference.py
index 5ac58cab9..789218e4e 100644
--- a/validmind/tests/model_validation/ragas/ContextPrecisionWithoutReference.py
+++ b/validmind/tests/model_validation/ragas/ContextPrecisionWithoutReference.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm", "retrieval_performance")
 @tasks("text_qa", "text_generation", "text_summarization", "text_classification")
 def ContextPrecisionWithoutReference(
-    dataset,
+    dataset: VMDataset,
     user_input_column: str = "user_input",
     retrieved_contexts_column: str = "retrieved_contexts",
     response_column: str = "response",
     judge_llm=None,
     judge_embeddings=None,
-):  # noqa: B950
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Context Precision Without Reference is a metric used to evaluate the relevance of
     retrieved contexts compared to the expected response for a given user input. This
diff --git a/validmind/tests/model_validation/ragas/ContextRecall.py b/validmind/tests/model_validation/ragas/ContextRecall.py
index 094927037..bdf681c99 100644
--- a/validmind/tests/model_validation/ragas/ContextRecall.py
+++ b/validmind/tests/model_validation/ragas/ContextRecall.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm", "retrieval_performance")
 @tasks("text_qa", "text_generation", "text_summarization", "text_classification")
 def ContextRecall(
-    dataset,
+    dataset: VMDataset,
     user_input_column: str = "user_input",
     retrieved_contexts_column: str = "retrieved_contexts",
     reference_column: str = "reference",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Context recall measures the extent to which the retrieved context aligns with the
     annotated answer, treated as the ground truth. It is computed based on the `ground
diff --git a/validmind/tests/model_validation/ragas/Faithfulness.py b/validmind/tests/model_validation/ragas/Faithfulness.py
index 6b83f26c8..f8fe77f50 100644
--- a/validmind/tests/model_validation/ragas/Faithfulness.py
+++ b/validmind/tests/model_validation/ragas/Faithfulness.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm", "rag_performance")
 @tasks("text_qa", "text_generation", "text_summarization")
 def Faithfulness(
-    dataset,
-    user_input_column="user_input",
-    response_column="response",
-    retrieved_contexts_column="retrieved_contexts",
+    dataset: VMDataset,
+    user_input_column: str = "user_input",
+    response_column: str = "response",
+    retrieved_contexts_column: str = "retrieved_contexts",
     judge_llm=None,
     judge_embeddings=None,
-):  # noqa
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Evaluates the faithfulness of the generated answers with respect to retrieved contexts.
 
diff --git a/validmind/tests/model_validation/ragas/NoiseSensitivity.py b/validmind/tests/model_validation/ragas/NoiseSensitivity.py
index 9a5d6e218..a6dd510d9 100644
--- a/validmind/tests/model_validation/ragas/NoiseSensitivity.py
+++ b/validmind/tests/model_validation/ragas/NoiseSensitivity.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -32,15 +35,15 @@
 @tags("ragas", "llm", "rag_performance")
 @tasks("text_qa", "text_generation", "text_summarization")
 def NoiseSensitivity(
-    dataset,
-    response_column="response",
-    retrieved_contexts_column="retrieved_contexts",
-    reference_column="reference",
-    focus="relevant",
-    user_input_column="user_input",
+    dataset: VMDataset,
+    response_column: str = "response",
+    retrieved_contexts_column: str = "retrieved_contexts",
+    reference_column: str = "reference",
+    focus: str = "relevant",
+    user_input_column: str = "user_input",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Assesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it
     generates incorrect responses.
diff --git a/validmind/tests/model_validation/ragas/ResponseRelevancy.py b/validmind/tests/model_validation/ragas/ResponseRelevancy.py
index 63d633355..6f9a9276c 100644
--- a/validmind/tests/model_validation/ragas/ResponseRelevancy.py
+++ b/validmind/tests/model_validation/ragas/ResponseRelevancy.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,13 +33,13 @@
 @tags("ragas", "llm", "rag_performance")
 @tasks("text_qa", "text_generation", "text_summarization")
 def ResponseRelevancy(
-    dataset,
-    user_input_column="user_input",
-    retrieved_contexts_column=None,
-    response_column="response",
+    dataset: VMDataset,
+    user_input_column: str = "user_input",
+    retrieved_contexts_column: str = None,
+    response_column: str = "response",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Assesses how pertinent the generated answer is to the given prompt.
 
diff --git a/validmind/tests/model_validation/ragas/SemanticSimilarity.py b/validmind/tests/model_validation/ragas/SemanticSimilarity.py
index c963dec5a..81ffe9588 100644
--- a/validmind/tests/model_validation/ragas/SemanticSimilarity.py
+++ b/validmind/tests/model_validation/ragas/SemanticSimilarity.py
@@ -3,12 +3,15 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import warnings
+from typing import Dict, Tuple
 
 import plotly.express as px
+import plotly.graph_objects as go
 from datasets import Dataset
 
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingDependencyError
+from validmind.vm_models import VMDataset
 
 from .utils import get_ragas_config, get_renamed_columns
 
@@ -30,12 +33,12 @@
 @tags("ragas", "llm")
 @tasks("text_qa", "text_generation", "text_summarization")
 def SemanticSimilarity(
-    dataset,
-    response_column="response",
-    reference_column="reference",
+    dataset: VMDataset,
+    response_column: str = "response",
+    reference_column: str = "reference",
     judge_llm=None,
     judge_embeddings=None,
-):
+) -> Tuple[Dict[str, list], go.Figure, go.Figure, RawData]:
     """
     Calculates the semantic similarity between generated responses and ground truths
 
diff --git a/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.py b/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.py
index 5792ffdef..cc5838d1f 100644
--- a/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.py
+++ b/validmind/tests/model_validation/sklearn/AdjustedMutualInformation.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import adjusted_mutual_info_score
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance", "clustering")
 @tasks("clustering")
-def AdjustedMutualInformation(model: VMModel, dataset: VMDataset):
+def AdjustedMutualInformation(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting
     for chance.
diff --git a/validmind/tests/model_validation/sklearn/AdjustedRandIndex.py b/validmind/tests/model_validation/sklearn/AdjustedRandIndex.py
index 7d529d8d9..572a17687 100644
--- a/validmind/tests/model_validation/sklearn/AdjustedRandIndex.py
+++ b/validmind/tests/model_validation/sklearn/AdjustedRandIndex.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import adjusted_rand_score
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance", "clustering")
 @tasks("clustering")
-def AdjustedRandIndex(model: VMModel, dataset: VMDataset):
+def AdjustedRandIndex(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine
     learning models.
diff --git a/validmind/tests/model_validation/sklearn/CalibrationCurve.py b/validmind/tests/model_validation/sklearn/CalibrationCurve.py
index 90bfd5bf8..52fbda934 100644
--- a/validmind/tests/model_validation/sklearn/CalibrationCurve.py
+++ b/validmind/tests/model_validation/sklearn/CalibrationCurve.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import plotly.graph_objects as go
 from sklearn.calibration import calibration_curve
 
@@ -12,7 +14,9 @@
 
 @tags("sklearn", "model_performance", "classification")
 @tasks("classification")
-def CalibrationCurve(model: VMModel, dataset: VMDataset, n_bins: int = 10):
+def CalibrationCurve(
+    model: VMModel, dataset: VMDataset, n_bins: int = 10
+) -> Tuple[go.Figure, RawData]:
     """
     Evaluates the calibration of probability estimates by comparing predicted probabilities against observed
     frequencies.
diff --git a/validmind/tests/model_validation/sklearn/ClassifierPerformance.py b/validmind/tests/model_validation/sklearn/ClassifierPerformance.py
index a5415d67a..6a4af3ca3 100644
--- a/validmind/tests/model_validation/sklearn/ClassifierPerformance.py
+++ b/validmind/tests/model_validation/sklearn/ClassifierPerformance.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List
+
 import numpy as np
 from sklearn.metrics import classification_report, roc_auc_score
 from sklearn.preprocessing import LabelBinarizer
@@ -20,7 +22,9 @@ def multiclass_roc_auc_score(y_test, y_pred, average="macro"):
     "sklearn", "binary_classification", "multiclass_classification", "model_performance"
 )
 @tasks("classification", "text_classification")
-def ClassifierPerformance(dataset: VMDataset, model: VMModel, average: str = "macro"):
+def ClassifierPerformance(
+    dataset: VMDataset, model: VMModel, average: str = "macro"
+) -> Dict[str, List[Dict[str, float]]]:
     """
     Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,
     and ROC AUC scores.
diff --git a/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.py b/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.py
index 6f393eaef..22e3e618d 100644
--- a/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.py
+++ b/validmind/tests/model_validation/sklearn/ClusterCosineSimilarity.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
 
@@ -12,7 +14,9 @@
 
 @tags("sklearn", "model_performance", "clustering")
 @tasks("clustering")
-def ClusterCosineSimilarity(model: VMModel, dataset: VMDataset):
+def ClusterCosineSimilarity(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Measures the intra-cluster similarity of a clustering model using cosine similarity.
 
diff --git a/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.py b/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.py
index ccb737be0..6c8c74dee 100644
--- a/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.py
+++ b/validmind/tests/model_validation/sklearn/ClusterPerformanceMetrics.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import (
     adjusted_mutual_info_score,
     adjusted_rand_score,
@@ -69,7 +71,9 @@
 
 @tags("sklearn", "model_performance", "clustering")
 @tasks("clustering")
-def ClusterPerformanceMetrics(model: VMModel, dataset: VMDataset):
+def ClusterPerformanceMetrics(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates the performance of clustering machine learning models using multiple established metrics.
 
diff --git a/validmind/tests/model_validation/sklearn/CompletenessScore.py b/validmind/tests/model_validation/sklearn/CompletenessScore.py
index ebf990ee4..190582e3a 100644
--- a/validmind/tests/model_validation/sklearn/CompletenessScore.py
+++ b/validmind/tests/model_validation/sklearn/CompletenessScore.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import completeness_score
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance", "clustering")
 @tasks("clustering")
-def CompletenessScore(model: VMModel, dataset: VMDataset):
+def CompletenessScore(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster.
 
diff --git a/validmind/tests/model_validation/sklearn/ConfusionMatrix.py b/validmind/tests/model_validation/sklearn/ConfusionMatrix.py
index 81017c87b..281cb0108 100644
--- a/validmind/tests/model_validation/sklearn/ConfusionMatrix.py
+++ b/validmind/tests/model_validation/sklearn/ConfusionMatrix.py
@@ -3,8 +3,11 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Tuple
+
 import numpy as np
 import plotly.figure_factory as ff
+import plotly.graph_objects as go
 from sklearn.metrics import confusion_matrix
 
 from validmind import RawData, tags, tasks
@@ -23,7 +26,7 @@ def ConfusionMatrix(
     dataset: VMDataset,
     model: VMModel,
     threshold: float = 0.5,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix
     heatmap.
diff --git a/validmind/tests/model_validation/sklearn/FeatureImportance.py b/validmind/tests/model_validation/sklearn/FeatureImportance.py
index 8f3ed77ca..4dff302f4 100644
--- a/validmind/tests/model_validation/sklearn/FeatureImportance.py
+++ b/validmind/tests/model_validation/sklearn/FeatureImportance.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 from sklearn.inspection import permutation_importance
 
@@ -11,7 +13,9 @@
 
 @tags("model_explainability", "sklearn")
 @tasks("regression", "time_series_forecasting")
-def FeatureImportance(dataset: VMDataset, model: VMModel, num_features: int = 3):
+def FeatureImportance(
+    dataset: VMDataset, model: VMModel, num_features: int = 3
+) -> Tuple[pd.DataFrame, RawData]:
     """
     Compute feature importance scores for a given model and generate a summary table
     with the top important features.
diff --git a/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.py b/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.py
index cf314fe83..485c3f028 100644
--- a/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.py
+++ b/validmind/tests/model_validation/sklearn/FowlkesMallowsScore.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("clustering")
-def FowlkesMallowsScore(dataset: VMDataset, model: VMModel):
+def FowlkesMallowsScore(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows
     score.
diff --git a/validmind/tests/model_validation/sklearn/HomogeneityScore.py b/validmind/tests/model_validation/sklearn/HomogeneityScore.py
index 739d6b086..31442cf68 100644
--- a/validmind/tests/model_validation/sklearn/HomogeneityScore.py
+++ b/validmind/tests/model_validation/sklearn/HomogeneityScore.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("clustering")
-def HomogeneityScore(dataset: VMDataset, model: VMModel):
+def HomogeneityScore(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1
     (homogeneous).
diff --git a/validmind/tests/model_validation/sklearn/HyperParametersTuning.py b/validmind/tests/model_validation/sklearn/HyperParametersTuning.py
index c12d12b29..0b93decd9 100644
--- a/validmind/tests/model_validation/sklearn/HyperParametersTuning.py
+++ b/validmind/tests/model_validation/sklearn/HyperParametersTuning.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import Dict, List, Union
+from typing import Dict, List, Tuple, Union
 
 from sklearn.metrics import make_scorer, recall_score
 from sklearn.model_selection import GridSearchCV
@@ -11,8 +11,6 @@
 from validmind.vm_models import VMDataset, VMModel
 
 
-@tags("sklearn", "model_performance")
-@tasks("classification", "clustering")
 def custom_recall(y_true, y_pred_proba, threshold=0.5):
     y_pred = (y_pred_proba >= threshold).astype(int)
     return recall_score(y_true, y_pred)
@@ -65,7 +63,7 @@ def HyperParametersTuning(
     scoring: Union[str, List, Dict] = None,
     thresholds: Union[float, List[float]] = None,
     fit_params: dict = None,
-):
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Performs exhaustive grid search over specified parameter ranges to find optimal model configurations
     across different metrics and decision thresholds.
diff --git a/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py b/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
index 485f93b34..6d7496451 100644
--- a/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
+++ b/validmind/tests/model_validation/sklearn/KMeansClustersOptimization.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List, Union
+from typing import List, Optional, Tuple
 
 import numpy as np
 import plotly.graph_objects as go
@@ -19,8 +19,8 @@
 @tags("sklearn", "model_performance", "kmeans")
 @tasks("clustering")
 def KMeansClustersOptimization(
-    model: VMModel, dataset: VMDataset, n_clusters: Union[List[int], None] = None
-):
+    model: VMModel, dataset: VMDataset, n_clusters: Optional[List[int]] = None
+) -> Tuple[go.Figure, RawData]:
     """
     Optimizes the number of clusters in K-means models using Elbow and Silhouette methods.
 
diff --git a/validmind/tests/model_validation/sklearn/MinimumAccuracy.py b/validmind/tests/model_validation/sklearn/MinimumAccuracy.py
index 7e470cfc1..16a87542e 100644
--- a/validmind/tests/model_validation/sklearn/MinimumAccuracy.py
+++ b/validmind/tests/model_validation/sklearn/MinimumAccuracy.py
@@ -1,6 +1,8 @@
 # Copyright © 2023-2024 ValidMind Inc. All rights reserved.
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import accuracy_score
 
 from validmind import RawData
@@ -12,7 +14,9 @@
     "sklearn", "binary_classification", "multiclass_classification", "model_performance"
 )
 @tasks("classification", "text_classification")
-def MinimumAccuracy(dataset: VMDataset, model: VMModel, min_threshold: float = 0.7):
+def MinimumAccuracy(
+    dataset: VMDataset, model: VMModel, min_threshold: float = 0.7
+) -> Tuple[List[Dict[str, float]], bool, RawData]:
     """
     Checks if the model's prediction accuracy meets or surpasses a specified threshold.
 
diff --git a/validmind/tests/model_validation/sklearn/MinimumF1Score.py b/validmind/tests/model_validation/sklearn/MinimumF1Score.py
index f3894d70c..1494fdaa8 100644
--- a/validmind/tests/model_validation/sklearn/MinimumF1Score.py
+++ b/validmind/tests/model_validation/sklearn/MinimumF1Score.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import numpy as np
 from sklearn.metrics import f1_score
 
@@ -14,7 +16,9 @@
     "sklearn", "binary_classification", "multiclass_classification", "model_performance"
 )
 @tasks("classification", "text_classification")
-def MinimumF1Score(dataset: VMDataset, model: VMModel, min_threshold: float = 0.5):
+def MinimumF1Score(
+    dataset: VMDataset, model: VMModel, min_threshold: float = 0.5
+) -> Tuple[List[Dict[str, float]], bool, RawData]:
     """
     Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced
     performance between precision and recall.
diff --git a/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.py b/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.py
index 61b945f10..f37a50f17 100644
--- a/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.py
+++ b/validmind/tests/model_validation/sklearn/MinimumROCAUCScore.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import numpy as np
 from sklearn.metrics import roc_auc_score
 from sklearn.preprocessing import LabelBinarizer
@@ -14,7 +16,9 @@
     "sklearn", "binary_classification", "multiclass_classification", "model_performance"
 )
 @tasks("classification", "text_classification")
-def MinimumROCAUCScore(dataset: VMDataset, model: VMModel, min_threshold: float = 0.5):
+def MinimumROCAUCScore(
+    dataset: VMDataset, model: VMModel, min_threshold: float = 0.5
+) -> Tuple[List[Dict[str, float]], bool, RawData]:
     """
     Validates model by checking if the ROC AUC score meets or surpasses a specified threshold.
 
diff --git a/validmind/tests/model_validation/sklearn/ModelParameters.py b/validmind/tests/model_validation/sklearn/ModelParameters.py
index f24d83dd5..789361381 100644
--- a/validmind/tests/model_validation/sklearn/ModelParameters.py
+++ b/validmind/tests/model_validation/sklearn/ModelParameters.py
@@ -2,14 +2,19 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List, Optional
+
 import pandas as pd
 
 from validmind import tags, tasks
+from validmind.vm_models import VMModel
 
 
 @tags("model_training", "metadata")
 @tasks("classification", "regression")
-def ModelParameters(model, model_params=None):
+def ModelParameters(
+    model: VMModel, model_params: Optional[List[str]] = None
+) -> pd.DataFrame:
     """
     Extracts and displays model parameters in a structured format for transparency and reproducibility.
 
diff --git a/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.py b/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.py
index a0f9002bb..69d524a6e 100644
--- a/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.py
+++ b/validmind/tests/model_validation/sklearn/ModelsPerformanceComparison.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List
+
 import numpy as np
 from sklearn.metrics import classification_report
 
@@ -19,7 +21,9 @@
     "model_comparison",
 )
 @tasks("classification", "text_classification")
-def ModelsPerformanceComparison(dataset: VMDataset, models: list[VMModel]):
+def ModelsPerformanceComparison(
+    dataset: VMDataset, models: list[VMModel]
+) -> Dict[str, List[Dict[str, float]]]:
     """
     Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,
     precision, recall, and F1 score.
diff --git a/validmind/tests/model_validation/sklearn/OverfitDiagnosis.py b/validmind/tests/model_validation/sklearn/OverfitDiagnosis.py
index 9994efd82..c7097b047 100644
--- a/validmind/tests/model_validation/sklearn/OverfitDiagnosis.py
+++ b/validmind/tests/model_validation/sklearn/OverfitDiagnosis.py
@@ -2,11 +2,12 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import plotly.graph_objects as go
 import seaborn as sns
 from sklearn import metrics
 
@@ -178,7 +179,7 @@ def OverfitDiagnosis(
     datasets: List[VMDataset],
     metric: str = None,
     cut_off_threshold: float = DEFAULT_THRESHOLD,
-):
+) -> Tuple[Dict[str, List[Dict[str, float]]], go.Figure, RawData]:
     """
     Assesses potential overfitting in a model's predictions, identifying regions where performance between training and
     testing sets deviates significantly.
diff --git a/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.py b/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.py
index 3f1d0d196..87ef2daa1 100644
--- a/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.py
+++ b/validmind/tests/model_validation/sklearn/PermutationFeatureImportance.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import Union
+from typing import Optional, Tuple
 
 import plotly.graph_objects as go
 from sklearn.inspection import permutation_importance
@@ -26,9 +26,9 @@
 def PermutationFeatureImportance(
     model: VMModel,
     dataset: VMDataset,
-    fontsize: Union[int, None] = None,
-    figure_height: Union[int, None] = None,
-):
+    fontsize: Optional[int] = None,
+    figure_height: Optional[int] = None,
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the significance of each feature in a model by evaluating the impact on model performance when feature
     values are randomly rearranged.
diff --git a/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.py b/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.py
index 3c3ac8ee6..45791bf34 100644
--- a/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.py
+++ b/validmind/tests/model_validation/sklearn/PopulationStabilityIndex.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -82,7 +82,7 @@ def calculate_psi(score_initial, score_new, num_bins=10, mode="fixed"):
 @tasks("classification", "text_classification")
 def PopulationStabilityIndex(
     datasets: List[VMDataset], model: VMModel, num_bins: int = 10, mode: str = "fixed"
-):
+) -> Tuple[Dict[str, List[Dict[str, float]]], go.Figure, RawData]:
     """
     Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across
     different datasets.
diff --git a/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.py b/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.py
index b521a333a..d40167c92 100644
--- a/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.py
+++ b/validmind/tests/model_validation/sklearn/PrecisionRecallCurve.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.graph_objects as go
 from sklearn.metrics import precision_recall_curve
@@ -14,7 +16,9 @@
 
 @tags("sklearn", "binary_classification", "model_performance", "visualization")
 @tasks("classification", "text_classification")
-def PrecisionRecallCurve(model: VMModel, dataset: VMDataset):
+def PrecisionRecallCurve(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[go.Figure, RawData]:
     """
     Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve.
 
diff --git a/validmind/tests/model_validation/sklearn/ROCCurve.py b/validmind/tests/model_validation/sklearn/ROCCurve.py
index 9f3161931..47b25df03 100644
--- a/validmind/tests/model_validation/sklearn/ROCCurve.py
+++ b/validmind/tests/model_validation/sklearn/ROCCurve.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.graph_objects as go
 from sklearn.metrics import roc_auc_score, roc_curve
@@ -19,7 +21,7 @@
     "visualization",
 )
 @tasks("classification", "text_classification")
-def ROCCurve(model: VMModel, dataset: VMDataset):
+def ROCCurve(model: VMModel, dataset: VMDataset) -> Tuple[go.Figure, RawData]:
     """
     Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic
     (ROC) curve and calculating the Area Under Curve (AUC) score.
diff --git a/validmind/tests/model_validation/sklearn/RegressionErrors.py b/validmind/tests/model_validation/sklearn/RegressionErrors.py
index f10ce3a48..a3610243a 100644
--- a/validmind/tests/model_validation/sklearn/RegressionErrors.py
+++ b/validmind/tests/model_validation/sklearn/RegressionErrors.py
@@ -2,16 +2,21 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("sklearn", "model_performance")
 @tasks("regression", "classification")
-def RegressionErrors(model, dataset):
+def RegressionErrors(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[pd.DataFrame, RawData]:
     """
     Assesses the performance and error distribution of a regression model using various error metrics.
 
diff --git a/validmind/tests/model_validation/sklearn/RegressionErrorsComparison.py b/validmind/tests/model_validation/sklearn/RegressionErrorsComparison.py
index 3a88c1405..be7dd8b74 100644
--- a/validmind/tests/model_validation/sklearn/RegressionErrorsComparison.py
+++ b/validmind/tests/model_validation/sklearn/RegressionErrorsComparison.py
@@ -2,19 +2,24 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List
+
 import numpy as np
 import pandas as pd
 from sklearn import metrics
 
 from validmind import tags, tasks
 from validmind.logging import get_logger
+from validmind.vm_models import VMDataset, VMModel
 
 logger = get_logger(__name__)
 
 
 @tags("model_performance", "sklearn")
 @tasks("regression", "time_series_forecasting")
-def RegressionErrorsComparison(datasets, models):
+def RegressionErrorsComparison(
+    datasets: List[VMDataset], models: List[VMModel]
+) -> pd.DataFrame:
     """
     Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing
     systematic overestimation or underestimation and large percentage errors.
diff --git a/validmind/tests/model_validation/sklearn/RegressionPerformance.py b/validmind/tests/model_validation/sklearn/RegressionPerformance.py
index 2c1eed0e4..67fea3fee 100644
--- a/validmind/tests/model_validation/sklearn/RegressionPerformance.py
+++ b/validmind/tests/model_validation/sklearn/RegressionPerformance.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import numpy as np
 from sklearn.metrics import mean_absolute_error, mean_squared_error
 
@@ -14,7 +16,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("regression")
-def RegressionPerformance(model: VMModel, dataset: VMDataset):
+def RegressionPerformance(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD.
 
diff --git a/validmind/tests/model_validation/sklearn/RegressionR2Square.py b/validmind/tests/model_validation/sklearn/RegressionR2Square.py
index 302184e6d..62fb65a9b 100644
--- a/validmind/tests/model_validation/sklearn/RegressionR2Square.py
+++ b/validmind/tests/model_validation/sklearn/RegressionR2Square.py
@@ -2,16 +2,21 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
 from validmind.tests.model_validation.statsmodels.statsutils import adj_r2_score
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("sklearn", "model_performance")
 @tasks("regression")
-def RegressionR2Square(dataset, model):
+def RegressionR2Square(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[pd.DataFrame, RawData]:
     """
     Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj
     R2) scores to determine the model's explanatory power over the dependent variable.
diff --git a/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.py b/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.py
index 5245bd8e2..03679c47a 100644
--- a/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.py
+++ b/validmind/tests/model_validation/sklearn/RegressionR2SquareComparison.py
@@ -2,16 +2,21 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List
+
 import pandas as pd
 from sklearn import metrics
 
 from validmind import tags, tasks
 from validmind.tests.model_validation.statsmodels.statsutils import adj_r2_score
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("model_performance", "sklearn")
 @tasks("regression", "time_series_forecasting")
-def RegressionR2SquareComparison(datasets, models):
+def RegressionR2SquareComparison(
+    datasets: List[VMDataset], models: List[VMModel]
+) -> pd.DataFrame:
     """
     Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess
     model performance and relevance of features.
diff --git a/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.py b/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.py
index 604e74815..b688d68a1 100644
--- a/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.py
+++ b/validmind/tests/model_validation/sklearn/RobustnessDiagnosis.py
@@ -12,9 +12,9 @@
 import seaborn as sns
 from sklearn import metrics
 
+from validmind import tags, tasks
 from validmind.errors import MissingOrInvalidModelPredictFnError
 from validmind.logging import get_logger
-from validmind.tests import tags, tasks
 from validmind.vm_models import VMDataset, VMModel
 
 logger = get_logger(__name__)
@@ -221,7 +221,7 @@ def RobustnessDiagnosis(
     metric: str = None,
     scaling_factor_std_dev_list: List[float] = DEFAULT_STD_DEV_LIST,
     performance_decay_threshold: float = DEFAULT_DECAY_THRESHOLD,
-):
+) -> Tuple[pd.DataFrame, go.Figure, bool]:
     """
     Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions.
 
diff --git a/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.py b/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.py
index 6c2d6c08b..452c1ed48 100644
--- a/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.py
+++ b/validmind/tests/model_validation/sklearn/ScoreProbabilityAlignment.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -13,7 +15,7 @@
 @tasks("classification")
 def ScoreProbabilityAlignment(
     model: VMModel, dataset: VMDataset, score_column: str = "score", n_bins: int = 10
-):
+) -> Tuple[pd.DataFrame, go.Figure, RawData]:
     """
     Analyzes the alignment between credit scores and predicted probabilities.
 
diff --git a/validmind/tests/model_validation/sklearn/SilhouettePlot.py b/validmind/tests/model_validation/sklearn/SilhouettePlot.py
index 2247a33ac..55273f0f1 100644
--- a/validmind/tests/model_validation/sklearn/SilhouettePlot.py
+++ b/validmind/tests/model_validation/sklearn/SilhouettePlot.py
@@ -2,8 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, Tuple
+
 import matplotlib.pyplot as plt
 import numpy as np
+import plotly.graph_objects as go
 from sklearn.metrics import silhouette_samples, silhouette_score
 
 from validmind import RawData, tags, tasks
@@ -12,7 +15,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("clustering")
-def SilhouettePlot(model: VMModel, dataset: VMDataset):
+def SilhouettePlot(
+    model: VMModel, dataset: VMDataset
+) -> Tuple[Dict[str, float], go.Figure, RawData]:
     """
     Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML
     models.
diff --git a/validmind/tests/model_validation/sklearn/TrainingTestDegradation.py b/validmind/tests/model_validation/sklearn/TrainingTestDegradation.py
index 1e023ea09..02eeec3fc 100644
--- a/validmind/tests/model_validation/sklearn/TrainingTestDegradation.py
+++ b/validmind/tests/model_validation/sklearn/TrainingTestDegradation.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 from numpy import unique
 from sklearn.metrics import classification_report
@@ -22,7 +22,7 @@
 @tasks("classification", "text_classification")
 def TrainingTestDegradation(
     datasets: List[VMDataset], model: VMModel, max_threshold: float = 0.10
-):
+) -> Tuple[List[Dict[str, float]], bool, RawData]:
     """
     Tests if model performance degradation between training and test datasets exceeds a predefined threshold.
 
diff --git a/validmind/tests/model_validation/sklearn/VMeasure.py b/validmind/tests/model_validation/sklearn/VMeasure.py
index 9153ed392..b51f8d656 100644
--- a/validmind/tests/model_validation/sklearn/VMeasure.py
+++ b/validmind/tests/model_validation/sklearn/VMeasure.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn import metrics
 
 from validmind import RawData, tags, tasks
@@ -10,7 +12,9 @@
 
 @tags("sklearn", "model_performance")
 @tasks("clustering")
-def VMeasure(dataset: VMDataset, model: VMModel):
+def VMeasure(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates homogeneity and completeness of a clustering model using the V Measure Score.
 
diff --git a/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py b/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py
index 591dccedb..6dc8a6180 100644
--- a/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py
+++ b/validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py
@@ -2,10 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import Callable, Dict, List, Tuple, Union
+from typing import Callable, Dict, List, Optional, Tuple
 
 import matplotlib.pyplot as plt
 import pandas as pd
+import plotly.graph_objects as go
 import seaborn as sns
 from sklearn import metrics
 
@@ -157,10 +158,10 @@ def _plot_weak_spots(
 def WeakspotsDiagnosis(
     datasets: List[VMDataset],
     model: VMModel,
-    features_columns: Union[List[str], None] = None,
-    metrics: Union[Dict[str, Callable], None] = None,
-    thresholds: Union[Dict[str, float], None] = None,
-):
+    features_columns: Optional[List[str]] = None,
+    metrics: Optional[Dict[str, Callable]] = None,
+    thresholds: Optional[Dict[str, float]] = None,
+) -> Tuple[pd.DataFrame, go.Figure, bool]:
     """
     Identifies and visualizes weak spots in a machine learning model's performance across various sections of the
     feature space.
diff --git a/validmind/tests/model_validation/statsmodels/AutoARIMA.py b/validmind/tests/model_validation/statsmodels/AutoARIMA.py
index b74626303..e63c70b87 100644
--- a/validmind/tests/model_validation/statsmodels/AutoARIMA.py
+++ b/validmind/tests/model_validation/statsmodels/AutoARIMA.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List
+
 from statsmodels.tsa.arima.model import ARIMA
 from statsmodels.tsa.stattools import adfuller
 
@@ -14,7 +16,7 @@
 
 @tags("time_series_data", "forecasting", "model_selection", "statsmodels")
 @tasks("regression")
-def AutoARIMA(model: VMModel, dataset: VMDataset):
+def AutoARIMA(model: VMModel, dataset: VMDataset) -> List[Dict[str, float]]:
     """
     Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria.
 
diff --git a/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.py b/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.py
index 733787529..bd5f1e4f4 100644
--- a/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.py
+++ b/validmind/tests/model_validation/statsmodels/CumulativePredictionProbabilities.py
@@ -2,16 +2,21 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import plotly.graph_objects as go
 from matplotlib import cm
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "credit_risk")
 @tasks("classification")
-def CumulativePredictionProbabilities(dataset, model, title="Cumulative Probabilities"):
+def CumulativePredictionProbabilities(
+    dataset: VMDataset, model: VMModel, title: str = "Cumulative Probabilities"
+) -> Tuple[go.Figure, RawData]:
     """
     Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models.
 
diff --git a/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.py b/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.py
index 02147456b..a2de47609 100644
--- a/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.py
+++ b/validmind/tests/model_validation/statsmodels/DurbinWatsonTest.py
@@ -2,15 +2,20 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List, Tuple
+
 import pandas as pd
 from statsmodels.stats.stattools import durbin_watson
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("regression")
 @tags("time_series_data", "forecasting", "statistical_test", "statsmodels")
-def DurbinWatsonTest(dataset, model, threshold=[1.5, 2.5]):
+def DurbinWatsonTest(
+    dataset: VMDataset, model: VMModel, threshold: List[float] = [1.5, 2.5]
+) -> Tuple[pd.DataFrame, RawData]:
     """
     Assesses autocorrelation in time series data features using the Durbin-Watson statistic.
 
diff --git a/validmind/tests/model_validation/statsmodels/GINITable.py b/validmind/tests/model_validation/statsmodels/GINITable.py
index 39a8815c3..54ab86a47 100644
--- a/validmind/tests/model_validation/statsmodels/GINITable.py
+++ b/validmind/tests/model_validation/statsmodels/GINITable.py
@@ -2,16 +2,19 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 from sklearn.metrics import roc_auc_score, roc_curve
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("model_performance")
 @tasks("classification")
-def GINITable(dataset, model):
+def GINITable(dataset: VMDataset, model: VMModel) -> Tuple[pd.DataFrame, RawData]:
     """
     Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets.
 
diff --git a/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.py b/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.py
index 61f76390b..46d94c82d 100644
--- a/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.py
+++ b/validmind/tests/model_validation/statsmodels/KolmogorovSmirnov.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from statsmodels.stats.diagnostic import kstest_normal
 
 from validmind import RawData, tags, tasks
@@ -11,7 +13,9 @@
 
 @tags("tabular_data", "data_distribution", "statistical_test", "statsmodels")
 @tasks("classification", "regression")
-def KolmogorovSmirnov(model: VMModel, dataset: VMDataset, dist: str = "norm"):
+def KolmogorovSmirnov(
+    model: VMModel, dataset: VMDataset, dist: str = "norm"
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test.
 
diff --git a/validmind/tests/model_validation/statsmodels/Lilliefors.py b/validmind/tests/model_validation/statsmodels/Lilliefors.py
index e51307d71..b7c3744b5 100644
--- a/validmind/tests/model_validation/statsmodels/Lilliefors.py
+++ b/validmind/tests/model_validation/statsmodels/Lilliefors.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List
+
 from statsmodels.stats.diagnostic import lilliefors
 
 from validmind import tags, tasks
@@ -10,7 +12,7 @@
 
 @tags("tabular_data", "data_distribution", "statistical_test", "statsmodels")
 @tasks("classification", "regression")
-def Lilliefors(dataset: VMDataset):
+def Lilliefors(dataset: VMDataset) -> List[Dict[str, float]]:
     """
     Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test.
 
diff --git a/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.py b/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.py
index 05887165d..820f7dfa5 100644
--- a/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.py
+++ b/validmind/tests/model_validation/statsmodels/PredictionProbabilitiesHistogram.py
@@ -2,18 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
 
 import plotly.graph_objects as go
 from matplotlib import cm
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization", "credit_risk")
 @tasks("classification")
 def PredictionProbabilitiesHistogram(
-    dataset, model, title="Histogram of Predictive Probabilities"
-):
+    dataset: VMDataset,
+    model: VMModel,
+    title: str = "Histogram of Predictive Probabilities",
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the predictive probability distribution for binary classification to evaluate model performance and
     potential overfitting or bias.
diff --git a/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py b/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
index aee3cbe90..8aa4a25c8 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionCoeffs.py
@@ -3,17 +3,20 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 from scipy import stats
 
 from validmind import RawData, tags, tasks
 from validmind.errors import SkipTestError
+from validmind.vm_models import VMModel
 
 
 @tags("tabular_data", "visualization", "model_training")
 @tasks("regression")
-def RegressionCoeffs(model):
+def RegressionCoeffs(model: VMModel) -> Tuple[go.Figure, RawData, pd.DataFrame]:
     """
     Assesses the significance and uncertainty of predictor variables in a regression model through visualization of
     coefficients and their 95% confidence intervals.
diff --git a/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.py b/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.py
index 3d017d1c6..28b326712 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionFeatureSignificance.py
@@ -2,7 +2,10 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import matplotlib.pyplot as plt
+import plotly.graph_objects as go
 import seaborn as sns
 
 from validmind import RawData, tags, tasks
@@ -16,8 +19,10 @@
 @tags("statistical_test", "model_interpretation", "visualization", "feature_importance")
 @tasks("regression")
 def RegressionFeatureSignificance(
-    model: VMModel, fontsize: int = 10, p_threshold: float = 0.05
-):
+    model: VMModel,
+    fontsize: int = 10,
+    p_threshold: float = 0.05,
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses and visualizes the statistical significance of features in a regression model.
 
diff --git a/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.py b/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.py
index 95bd0fdd4..1290447f7 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlot.py
@@ -2,10 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import Union
+from typing import Optional, Tuple
 
 import matplotlib.pyplot as plt
 import pandas as pd
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.logging import get_logger
@@ -19,9 +20,9 @@
 def RegressionModelForecastPlot(
     model: VMModel,
     dataset: VMDataset,
-    start_date: Union[str, None] = None,
-    end_date: Union[str, None] = None,
-):
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+) -> Tuple[go.Figure, RawData]:
     """
     Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over
     a specified date range.
diff --git a/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.py b/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.py
index 467ea1440..24afb4a3e 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionModelForecastPlotLevels.py
@@ -2,9 +2,12 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.vm_models import VMDataset, VMModel
@@ -23,7 +26,7 @@ def integrate_diff(series_diff, start_value):
 def RegressionModelForecastPlotLevels(
     model: VMModel,
     dataset: VMDataset,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the alignment between forecasted and observed values in regression models through visual plots
 
diff --git a/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.py b/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.py
index d8f5c120d..cd16e6f55 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionModelSensitivityPlot.py
@@ -2,10 +2,11 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List, Union
+from typing import List, Tuple, Union
 
 import matplotlib.pyplot as plt
 import numpy as np
+import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
 from validmind.logging import get_logger
@@ -29,7 +30,7 @@ def RegressionModelSensitivityPlot(
     model: VMModel,
     shocks: List[float] = [0.1],
     transformation: Union[str, None] = None,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and
     visualizing the impact.
diff --git a/validmind/tests/model_validation/statsmodels/RegressionModelSummary.py b/validmind/tests/model_validation/statsmodels/RegressionModelSummary.py
index 88d0b9162..2d40d06f6 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionModelSummary.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionModelSummary.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 from sklearn.metrics import mean_squared_error, r2_score
 
 from validmind import RawData, tags, tasks
@@ -12,7 +14,9 @@
 
 @tags("model_performance", "regression")
 @tasks("regression")
-def RegressionModelSummary(dataset: VMDataset, model: VMModel):
+def RegressionModelSummary(
+    dataset: VMDataset, model: VMModel
+) -> Tuple[List[Dict[str, float]], RawData]:
     """
     Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE.
 
diff --git a/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.py b/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.py
index 108cd45cf..27b66ba5e 100644
--- a/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.py
+++ b/validmind/tests/model_validation/statsmodels/RegressionPermutationFeatureImportance.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
@@ -19,7 +21,7 @@
 @tasks("regression")
 def RegressionPermutationFeatureImportance(
     dataset: VMDataset, model: VMModel, fontsize: int = 12, figure_height: int = 500
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Assesses the significance of each feature in a model by evaluating the impact on model performance when feature
     values are randomly rearranged.
diff --git a/validmind/tests/model_validation/statsmodels/ScorecardHistogram.py b/validmind/tests/model_validation/statsmodels/ScorecardHistogram.py
index 29678e5af..4e08b321a 100644
--- a/validmind/tests/model_validation/statsmodels/ScorecardHistogram.py
+++ b/validmind/tests/model_validation/statsmodels/ScorecardHistogram.py
@@ -6,11 +6,16 @@
 from matplotlib import cm
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset
 
 
 @tags("visualization", "credit_risk", "logistic_regression")
 @tasks("classification")
-def ScorecardHistogram(dataset, title="Histogram of Scores", score_column="score"):
+def ScorecardHistogram(
+    dataset: VMDataset,
+    title: str = "Histogram of Scores",
+    score_column: str = "score",
+) -> go.Figure:
     """
     The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,
     providing critical insights into the performance and generalizability of credit-risk models.
diff --git a/validmind/tests/ongoing_monitoring/CalibrationCurveDrift.py b/validmind/tests/ongoing_monitoring/CalibrationCurveDrift.py
index f29848137..7e2482c04 100644
--- a/validmind/tests/ongoing_monitoring/CalibrationCurveDrift.py
+++ b/validmind/tests/ongoing_monitoring/CalibrationCurveDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -26,7 +26,7 @@ def CalibrationCurveDrift(
     model: VMModel,
     n_bins: int = 10,
     drift_pct_threshold: float = 20,
-):
+) -> Tuple[go.Figure, Dict[str, pd.DataFrame], bool, RawData]:
     """
     Evaluates changes in probability calibration between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py b/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
index 9e9e62cf4..ee48eedaf 100644
--- a/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
+++ b/validmind/tests/ongoing_monitoring/ClassDiscriminationDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -38,7 +38,7 @@ def calculate_ks_statistic(y_true, y_prob):
 @tasks("classification", "text_classification")
 def ClassDiscriminationDrift(
     datasets: List[VMDataset], model: VMModel, drift_pct_threshold=20
-):
+) -> Tuple[Dict[str, pd.DataFrame], bool]:
     """
     Compares classification discrimination metrics between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/ClassImbalanceDrift.py b/validmind/tests/ongoing_monitoring/ClassImbalanceDrift.py
index fa59efbf7..042738911 100644
--- a/validmind/tests/ongoing_monitoring/ClassImbalanceDrift.py
+++ b/validmind/tests/ongoing_monitoring/ClassImbalanceDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import pandas as pd
 import plotly.graph_objs as go
@@ -18,7 +18,7 @@ def ClassImbalanceDrift(
     datasets: List[VMDataset],
     drift_pct_threshold: float = 5.0,
     title: str = "Class Distribution Drift",
-):
+) -> Tuple[go.Figure, Dict[str, pd.DataFrame], bool]:
     """
     Evaluates drift in class distribution between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/ClassificationAccuracyDrift.py b/validmind/tests/ongoing_monitoring/ClassificationAccuracyDrift.py
index 85b43a576..37ec3e670 100644
--- a/validmind/tests/ongoing_monitoring/ClassificationAccuracyDrift.py
+++ b/validmind/tests/ongoing_monitoring/ClassificationAccuracyDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -18,7 +18,7 @@
 @tasks("classification", "text_classification")
 def ClassificationAccuracyDrift(
     datasets: List[VMDataset], model: VMModel, drift_pct_threshold=20
-):
+) -> Tuple[Dict[str, pd.DataFrame], bool, RawData]:
     """
     Compares classification accuracy metrics between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/ConfusionMatrixDrift.py b/validmind/tests/ongoing_monitoring/ConfusionMatrixDrift.py
index 37a1a2ac0..c905555e9 100644
--- a/validmind/tests/ongoing_monitoring/ConfusionMatrixDrift.py
+++ b/validmind/tests/ongoing_monitoring/ConfusionMatrixDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -18,7 +18,7 @@
 @tasks("classification", "text_classification")
 def ConfusionMatrixDrift(
     datasets: List[VMDataset], model: VMModel, drift_pct_threshold=20
-):
+) -> Tuple[Dict[str, pd.DataFrame], bool, RawData]:
     """
     Compares confusion matrix metrics between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/CumulativePredictionProbabilitiesDrift.py b/validmind/tests/ongoing_monitoring/CumulativePredictionProbabilitiesDrift.py
index d4143c5d7..1b03a0b81 100644
--- a/validmind/tests/ongoing_monitoring/CumulativePredictionProbabilitiesDrift.py
+++ b/validmind/tests/ongoing_monitoring/CumulativePredictionProbabilitiesDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import List, Tuple
 
 import numpy as np
 import plotly.graph_objects as go
@@ -17,7 +17,7 @@
 def CumulativePredictionProbabilitiesDrift(
     datasets: List[VMDataset],
     model: VMModel,
-):
+) -> Tuple[go.Figure, RawData]:
     """
     Compares cumulative prediction probability distributions between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/FeatureDrift.py b/validmind/tests/ongoing_monitoring/FeatureDrift.py
index 52547e2f7..9df0cf17f 100644
--- a/validmind/tests/ongoing_monitoring/FeatureDrift.py
+++ b/validmind/tests/ongoing_monitoring/FeatureDrift.py
@@ -2,11 +2,14 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset
 
 
 def calculate_psi_score(actual, expected):
@@ -92,11 +95,11 @@ def create_distribution_plot(feature_name, reference_dist, monitoring_dist, bins
 @tags("visualization")
 @tasks("monitoring")
 def FeatureDrift(
-    datasets,
+    datasets: List[VMDataset],
     bins=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
     feature_columns=None,
     psi_threshold=0.2,
-):
+) -> Tuple[Dict[str, pd.DataFrame], go.Figure, bool, RawData]:
     """
     Evaluates changes in feature distribution over time to identify potential model drift.
 
diff --git a/validmind/tests/ongoing_monitoring/PredictionAcrossEachFeature.py b/validmind/tests/ongoing_monitoring/PredictionAcrossEachFeature.py
index 10c075c78..c3c79169d 100644
--- a/validmind/tests/ongoing_monitoring/PredictionAcrossEachFeature.py
+++ b/validmind/tests/ongoing_monitoring/PredictionAcrossEachFeature.py
@@ -3,14 +3,19 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 
+from typing import List, Tuple
+
 import matplotlib.pyplot as plt
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization")
 @tasks("monitoring")
-def PredictionAcrossEachFeature(datasets, model):
+def PredictionAcrossEachFeature(
+    datasets: List[VMDataset], model: VMModel
+) -> Tuple[plt.Figure, RawData]:
     """
     Assesses differences in model predictions across individual features between reference and monitoring datasets
     through visual analysis.
diff --git a/validmind/tests/ongoing_monitoring/PredictionCorrelation.py b/validmind/tests/ongoing_monitoring/PredictionCorrelation.py
index ddbc14503..937bb92ab 100644
--- a/validmind/tests/ongoing_monitoring/PredictionCorrelation.py
+++ b/validmind/tests/ongoing_monitoring/PredictionCorrelation.py
@@ -2,15 +2,22 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import pandas as pd
 import plotly.graph_objects as go
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization")
 @tasks("monitoring")
-def PredictionCorrelation(datasets, model, drift_pct_threshold=20):
+def PredictionCorrelation(
+    datasets: List[VMDataset],
+    model: VMModel,
+    drift_pct_threshold: float = 20,
+) -> Tuple[Dict[str, pd.DataFrame], go.Figure, bool, RawData]:
     """
     Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential
     target drift.
diff --git a/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py b/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
index 150e544be..e3b56f41e 100644
--- a/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
+++ b/validmind/tests/ongoing_monitoring/PredictionProbabilitiesHistogramDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -21,7 +21,7 @@ def PredictionProbabilitiesHistogramDrift(
     model: VMModel,
     title="Prediction Probabilities Histogram Drift",
     drift_pct_threshold: float = 20.0,
-):
+) -> Tuple[go.Figure, Dict[str, pd.DataFrame], bool, RawData]:
     """
     Compares prediction probability distributions between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/PredictionQuantilesAcrossFeatures.py b/validmind/tests/ongoing_monitoring/PredictionQuantilesAcrossFeatures.py
index 9d24dfc91..c40c71f93 100644
--- a/validmind/tests/ongoing_monitoring/PredictionQuantilesAcrossFeatures.py
+++ b/validmind/tests/ongoing_monitoring/PredictionQuantilesAcrossFeatures.py
@@ -2,15 +2,20 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import List, Tuple
+
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization")
 @tasks("monitoring")
-def PredictionQuantilesAcrossFeatures(datasets, model):
+def PredictionQuantilesAcrossFeatures(
+    datasets: List[VMDataset], model: VMModel
+) -> Tuple[go.Figure, ...]:
     """
     Assesses differences in model prediction distributions across individual features between reference
     and monitoring datasets through quantile analysis.
diff --git a/validmind/tests/ongoing_monitoring/ROCCurveDrift.py b/validmind/tests/ongoing_monitoring/ROCCurveDrift.py
index b2cc2e335..d76cb9e65 100644
--- a/validmind/tests/ongoing_monitoring/ROCCurveDrift.py
+++ b/validmind/tests/ongoing_monitoring/ROCCurveDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import List, Tuple
 
 import numpy as np
 import plotly.graph_objects as go
@@ -20,7 +20,9 @@
     "visualization",
 )
 @tasks("classification", "text_classification")
-def ROCCurveDrift(datasets: List[VMDataset], model: VMModel):
+def ROCCurveDrift(
+    datasets: List[VMDataset], model: VMModel
+) -> Tuple[go.Figure, go.Figure, RawData]:
     """
     Compares ROC curves between reference and monitoring datasets.
 
diff --git a/validmind/tests/ongoing_monitoring/ScoreBandsDrift.py b/validmind/tests/ongoing_monitoring/ScoreBandsDrift.py
index b122e26bd..23874934f 100644
--- a/validmind/tests/ongoing_monitoring/ScoreBandsDrift.py
+++ b/validmind/tests/ongoing_monitoring/ScoreBandsDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -19,7 +19,7 @@ def ScoreBandsDrift(
     score_column: str = "score",
     score_bands: list = None,
     drift_threshold: float = 20.0,
-):
+) -> Tuple[Dict[str, pd.DataFrame], bool, RawData]:
     """
     Analyzes drift in population distribution and default rates across score bands.
 
diff --git a/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py b/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
index 09aa43491..ded42cf64 100644
--- a/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
+++ b/validmind/tests/ongoing_monitoring/ScorecardHistogramDrift.py
@@ -2,7 +2,7 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
-from typing import List
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -21,7 +21,7 @@ def ScorecardHistogramDrift(
     score_column: str = "score",
     title: str = "Scorecard Histogram Drift",
     drift_pct_threshold: float = 20.0,
-):
+) -> Tuple[go.Figure, Dict[str, pd.DataFrame], bool]:
     """
     Compares score distributions between reference and monitoring datasets for each class.
 
diff --git a/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py b/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
index d3eba152b..55beee0aa 100644
--- a/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
+++ b/validmind/tests/ongoing_monitoring/TargetPredictionDistributionPlot.py
@@ -2,17 +2,24 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Dict, List, Tuple
+
 import pandas as pd
 import plotly.figure_factory as ff
 import plotly.graph_objects as go
 from scipy.stats import kurtosis, skew
 
 from validmind import RawData, tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("visualization")
 @tasks("monitoring")
-def TargetPredictionDistributionPlot(datasets, model, drift_pct_threshold=20):
+def TargetPredictionDistributionPlot(
+    datasets: List[VMDataset],
+    model: VMModel,
+    drift_pct_threshold: float = 20,
+) -> Tuple[Dict[str, pd.DataFrame], go.Figure, bool, RawData]:
     """
     Assesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify
     potential data drift.
diff --git a/validmind/tests/prompt_validation/Bias.py b/validmind/tests/prompt_validation/Bias.py
index 275894ae3..80d5ed401 100644
--- a/validmind/tests/prompt_validation/Bias.py
+++ b/validmind/tests/prompt_validation/Bias.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -45,7 +47,9 @@
 
 @tags("llm", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Bias(model, min_threshold=7, judge_llm=None):
+def Bias(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the
     prompt.
diff --git a/validmind/tests/prompt_validation/Clarity.py b/validmind/tests/prompt_validation/Clarity.py
index 63acfd5bf..a55f8819a 100644
--- a/validmind/tests/prompt_validation/Clarity.py
+++ b/validmind/tests/prompt_validation/Clarity.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -46,7 +48,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Clarity(model, min_threshold=7, judge_llm=None):
+def Clarity(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines.
 
diff --git a/validmind/tests/prompt_validation/Conciseness.py b/validmind/tests/prompt_validation/Conciseness.py
index cc26d1769..7822375d9 100644
--- a/validmind/tests/prompt_validation/Conciseness.py
+++ b/validmind/tests/prompt_validation/Conciseness.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -54,7 +56,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Conciseness(model, min_threshold=7, judge_llm=None):
+def Conciseness(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Analyzes and grades the conciseness of prompts provided to a Large Language Model.
 
diff --git a/validmind/tests/prompt_validation/Delimitation.py b/validmind/tests/prompt_validation/Delimitation.py
index 87ae8e260..a702e57a6 100644
--- a/validmind/tests/prompt_validation/Delimitation.py
+++ b/validmind/tests/prompt_validation/Delimitation.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -39,7 +41,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Delimitation(model, min_threshold=7, judge_llm=None):
+def Delimitation(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Evaluates the proper use of delimiters in prompts provided to Large Language Models.
 
diff --git a/validmind/tests/prompt_validation/NegativeInstruction.py b/validmind/tests/prompt_validation/NegativeInstruction.py
index 64c4a2338..8b66f3fa6 100644
--- a/validmind/tests/prompt_validation/NegativeInstruction.py
+++ b/validmind/tests/prompt_validation/NegativeInstruction.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -52,7 +54,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def NegativeInstruction(model, min_threshold=7, judge_llm=None):
+def NegativeInstruction(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts.
 
diff --git a/validmind/tests/prompt_validation/Robustness.py b/validmind/tests/prompt_validation/Robustness.py
index d1e306f5d..09533c5a3 100644
--- a/validmind/tests/prompt_validation/Robustness.py
+++ b/validmind/tests/prompt_validation/Robustness.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 import pandas as pd
 
 from validmind import RawData, tags, tasks
@@ -56,7 +58,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Robustness(model, dataset, num_tests=10, judge_llm=None):
+def Robustness(
+    model, dataset, num_tests=10, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test
     specifically measures the model's ability to generate correct classifications with the given prompt even when the
diff --git a/validmind/tests/prompt_validation/Specificity.py b/validmind/tests/prompt_validation/Specificity.py
index 10cf4ee5a..54d54b197 100644
--- a/validmind/tests/prompt_validation/Specificity.py
+++ b/validmind/tests/prompt_validation/Specificity.py
@@ -2,6 +2,8 @@
 # See the LICENSE file in the root of this repository for details.
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
+from typing import Any, Dict, List, Tuple
+
 from validmind import RawData, tags, tasks
 from validmind.errors import MissingRequiredTestInputError
 
@@ -52,7 +54,9 @@
 
 @tags("llm", "zero_shot", "few_shot")
 @tasks("text_classification", "text_summarization")
-def Specificity(model, min_threshold=7, judge_llm=None):
+def Specificity(
+    model, min_threshold=7, judge_llm=None
+) -> Tuple[List[Dict[str, Any]], bool, RawData]:
     """
     Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,
     and relevance.
diff --git a/validmind/unit_metrics/classification/Accuracy.py b/validmind/unit_metrics/classification/Accuracy.py
index 5bd3f6d95..a341c99f7 100644
--- a/validmind/unit_metrics/classification/Accuracy.py
+++ b/validmind/unit_metrics/classification/Accuracy.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import accuracy_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("classification")
 @tags("classification")
-def Accuracy(dataset, model):
+def Accuracy(dataset: VMDataset, model: VMModel) -> float:
     """Calculates the accuracy of a model"""
     return accuracy_score(dataset.y, dataset.y_pred(model))
diff --git a/validmind/unit_metrics/classification/F1.py b/validmind/unit_metrics/classification/F1.py
index 62bda0d30..ea302571a 100644
--- a/validmind/unit_metrics/classification/F1.py
+++ b/validmind/unit_metrics/classification/F1.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import f1_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("classification")
 @tags("classification")
-def F1(model, dataset, **kwargs):
+def F1(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the F1 score for a classification model."""
     return f1_score(dataset.y, dataset.y_pred(model), **kwargs)
diff --git a/validmind/unit_metrics/classification/Precision.py b/validmind/unit_metrics/classification/Precision.py
index fa35504ce..3523d080d 100644
--- a/validmind/unit_metrics/classification/Precision.py
+++ b/validmind/unit_metrics/classification/Precision.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import precision_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("classification")
 @tags("classification")
-def Precision(model, dataset, **kwargs):
+def Precision(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the precision for a classification model."""
     return precision_score(dataset.y, dataset.y_pred(model), **kwargs)
diff --git a/validmind/unit_metrics/classification/ROC_AUC.py b/validmind/unit_metrics/classification/ROC_AUC.py
index d362369e2..1abdb07b5 100644
--- a/validmind/unit_metrics/classification/ROC_AUC.py
+++ b/validmind/unit_metrics/classification/ROC_AUC.py
@@ -7,11 +7,12 @@
 from sklearn.preprocessing import LabelBinarizer
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("classification")
 @tags("classification")
-def ROC_AUC(model, dataset, **kwargs):
+def ROC_AUC(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the ROC AUC for a classification model."""
     y_true = dataset.y
 
diff --git a/validmind/unit_metrics/classification/Recall.py b/validmind/unit_metrics/classification/Recall.py
index a0668142f..6f88e4e05 100644
--- a/validmind/unit_metrics/classification/Recall.py
+++ b/validmind/unit_metrics/classification/Recall.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import recall_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tasks("classification")
 @tags("classification")
-def Recall(model, dataset, **kwargs):
+def Recall(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the recall for a classification model."""
     return recall_score(dataset.y, dataset.y_pred(model), **kwargs)
diff --git a/validmind/unit_metrics/regression/AdjustedRSquaredScore.py b/validmind/unit_metrics/regression/AdjustedRSquaredScore.py
index b4003e132..ef0507254 100644
--- a/validmind/unit_metrics/regression/AdjustedRSquaredScore.py
+++ b/validmind/unit_metrics/regression/AdjustedRSquaredScore.py
@@ -5,11 +5,12 @@
 from sklearn.metrics import r2_score as _r2_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def AdjustedRSquaredScore(model, dataset):
+def AdjustedRSquaredScore(model: VMModel, dataset: VMDataset) -> float:
     """Calculates the adjusted R-squared score for a regression model."""
     r2_score = _r2_score(
         dataset.y,
diff --git a/validmind/unit_metrics/regression/GiniCoefficient.py b/validmind/unit_metrics/regression/GiniCoefficient.py
index eee6afd88..a40a58c22 100644
--- a/validmind/unit_metrics/regression/GiniCoefficient.py
+++ b/validmind/unit_metrics/regression/GiniCoefficient.py
@@ -5,11 +5,12 @@
 import numpy as np
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def GiniCoefficient(dataset, model):
+def GiniCoefficient(dataset: VMDataset, model: VMModel) -> float:
     """Calculates the Gini coefficient for a regression model."""
     y_true = dataset.y
     y_pred = dataset.y_pred(model)
diff --git a/validmind/unit_metrics/regression/HuberLoss.py b/validmind/unit_metrics/regression/HuberLoss.py
index 511d2e650..8db2d2864 100644
--- a/validmind/unit_metrics/regression/HuberLoss.py
+++ b/validmind/unit_metrics/regression/HuberLoss.py
@@ -5,11 +5,12 @@
 import numpy as np
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def HuberLoss(model, dataset):
+def HuberLoss(model: VMModel, dataset: VMDataset) -> float:
     """Calculates the Huber loss for a regression model."""
     y_true = dataset.y
     y_pred = dataset.y_pred(model)
diff --git a/validmind/unit_metrics/regression/KolmogorovSmirnovStatistic.py b/validmind/unit_metrics/regression/KolmogorovSmirnovStatistic.py
index c51a6171f..817ae4f72 100644
--- a/validmind/unit_metrics/regression/KolmogorovSmirnovStatistic.py
+++ b/validmind/unit_metrics/regression/KolmogorovSmirnovStatistic.py
@@ -5,11 +5,12 @@
 import numpy as np
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def KolmogorovSmirnovStatistic(dataset, model):
+def KolmogorovSmirnovStatistic(dataset: VMDataset, model: VMModel) -> float:
     """Calculates the Kolmogorov-Smirnov statistic for a regression model."""
     y_true = dataset.y.flatten()
     y_pred = dataset.y_pred(model)
diff --git a/validmind/unit_metrics/regression/MeanAbsoluteError.py b/validmind/unit_metrics/regression/MeanAbsoluteError.py
index fc29282ed..94aac7972 100644
--- a/validmind/unit_metrics/regression/MeanAbsoluteError.py
+++ b/validmind/unit_metrics/regression/MeanAbsoluteError.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import mean_absolute_error as _mean_absolute_error
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def MeanAbsoluteError(model, dataset, **kwargs):
+def MeanAbsoluteError(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the mean absolute error for a regression model."""
     return _mean_absolute_error(dataset.y, dataset.y_pred(model), **kwargs)
diff --git a/validmind/unit_metrics/regression/MeanAbsolutePercentageError.py b/validmind/unit_metrics/regression/MeanAbsolutePercentageError.py
index 2e0b1b700..e6703c3ab 100644
--- a/validmind/unit_metrics/regression/MeanAbsolutePercentageError.py
+++ b/validmind/unit_metrics/regression/MeanAbsolutePercentageError.py
@@ -5,11 +5,12 @@
 import numpy as np
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def MeanAbsolutePercentageError(model, dataset):
+def MeanAbsolutePercentageError(model: VMModel, dataset: VMDataset) -> float:
     """Calculates the mean absolute percentage error for a regression model."""
     y_true = dataset.y
     y_pred = dataset.y_pred(model)
diff --git a/validmind/unit_metrics/regression/MeanBiasDeviation.py b/validmind/unit_metrics/regression/MeanBiasDeviation.py
index 0ae616bc7..446e9b620 100644
--- a/validmind/unit_metrics/regression/MeanBiasDeviation.py
+++ b/validmind/unit_metrics/regression/MeanBiasDeviation.py
@@ -5,10 +5,11 @@
 import numpy as np
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def MeanBiasDeviation(model, dataset):
+def MeanBiasDeviation(model: VMModel, dataset: VMDataset) -> float:
     """Calculates the mean bias deviation for a regression model."""
     return np.mean(dataset.y - dataset.y_pred(model))
diff --git a/validmind/unit_metrics/regression/MeanSquaredError.py b/validmind/unit_metrics/regression/MeanSquaredError.py
index 2d49e38a2..b4943b95a 100644
--- a/validmind/unit_metrics/regression/MeanSquaredError.py
+++ b/validmind/unit_metrics/regression/MeanSquaredError.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import mean_squared_error
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def MeanSquaredError(model, dataset, **kwargs):
+def MeanSquaredError(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the mean squared error for a regression model."""
     return mean_squared_error(dataset.y, dataset.y_pred(model), **kwargs)
diff --git a/validmind/unit_metrics/regression/QuantileLoss.py b/validmind/unit_metrics/regression/QuantileLoss.py
index 35967fcd8..0c2b86826 100644
--- a/validmind/unit_metrics/regression/QuantileLoss.py
+++ b/validmind/unit_metrics/regression/QuantileLoss.py
@@ -9,7 +9,7 @@
 
 @tags("regression")
 @tasks("regression")
-def QuantileLoss(model, dataset, quantile=0.5):
+def QuantileLoss(model, dataset, quantile=0.5) -> float:
     """Calculates the quantile loss for a regression model."""
     error = dataset.y - dataset.y_pred(model)
 
diff --git a/validmind/unit_metrics/regression/RSquaredScore.py b/validmind/unit_metrics/regression/RSquaredScore.py
index 85df74433..1d53212ae 100644
--- a/validmind/unit_metrics/regression/RSquaredScore.py
+++ b/validmind/unit_metrics/regression/RSquaredScore.py
@@ -5,10 +5,11 @@
 from sklearn.metrics import r2_score
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def RSquaredScore(model, dataset):
+def RSquaredScore(model: VMModel, dataset: VMDataset) -> float:
     """Calculates the R-squared score for a regression model."""
     return r2_score(dataset.y, dataset.y_pred(model))
diff --git a/validmind/unit_metrics/regression/RootMeanSquaredError.py b/validmind/unit_metrics/regression/RootMeanSquaredError.py
index 146a36da0..d387139b6 100644
--- a/validmind/unit_metrics/regression/RootMeanSquaredError.py
+++ b/validmind/unit_metrics/regression/RootMeanSquaredError.py
@@ -6,11 +6,12 @@
 from sklearn.metrics import mean_squared_error
 
 from validmind import tags, tasks
+from validmind.vm_models import VMDataset, VMModel
 
 
 @tags("regression")
 @tasks("regression")
-def RootMeanSquaredError(model, dataset, **kwargs):
+def RootMeanSquaredError(model: VMModel, dataset: VMDataset, **kwargs) -> float:
     """Calculates the root mean squared error for a regression model."""
     return np.sqrt(
         mean_squared_error(