diff --git a/notebooks/how_to/explore_tests.ipynb b/notebooks/how_to/explore_tests.ipynb
index 9a60f9c08..672c98fc3 100644
--- a/notebooks/how_to/explore_tests.ipynb
+++ b/notebooks/how_to/explore_tests.ipynb
@@ -81,1191 +81,1786 @@
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#T_87ae0_row184_col5, #T_87ae0_row184_col6, #T_87ae0_row185_col0, #T_87ae0_row185_col1, #T_87ae0_row185_col2, #T_87ae0_row185_col3, #T_87ae0_row185_col4, #T_87ae0_row185_col5, #T_87ae0_row185_col6, #T_87ae0_row186_col0, #T_87ae0_row186_col1, #T_87ae0_row186_col2, #T_87ae0_row186_col3, #T_87ae0_row186_col4, #T_87ae0_row186_col5, #T_87ae0_row186_col6, #T_87ae0_row187_col0, #T_87ae0_row187_col1, #T_87ae0_row187_col2, #T_87ae0_row187_col3, #T_87ae0_row187_col4, #T_87ae0_row187_col5, #T_87ae0_row187_col6, #T_87ae0_row188_col0, #T_87ae0_row188_col1, #T_87ae0_row188_col2, #T_87ae0_row188_col3, #T_87ae0_row188_col4, #T_87ae0_row188_col5, #T_87ae0_row188_col6, #T_87ae0_row189_col0, #T_87ae0_row189_col1, #T_87ae0_row189_col2, #T_87ae0_row189_col3, #T_87ae0_row189_col4, #T_87ae0_row189_col5, #T_87ae0_row189_col6, #T_87ae0_row190_col0, #T_87ae0_row190_col1, #T_87ae0_row190_col2, #T_87ae0_row190_col3, #T_87ae0_row190_col4, #T_87ae0_row190_col5, #T_87ae0_row190_col6, #T_87ae0_row191_col0, #T_87ae0_row191_col1, #T_87ae0_row191_col2, #T_87ae0_row191_col3, #T_87ae0_row191_col4, #T_87ae0_row191_col5, #T_87ae0_row191_col6, #T_87ae0_row192_col0, #T_87ae0_row192_col1, #T_87ae0_row192_col2, #T_87ae0_row192_col3, #T_87ae0_row192_col4, #T_87ae0_row192_col5, #T_87ae0_row192_col6, #T_87ae0_row193_col0, #T_87ae0_row193_col1, #T_87ae0_row193_col2, #T_87ae0_row193_col3, #T_87ae0_row193_col4, #T_87ae0_row193_col5, #T_87ae0_row193_col6, #T_87ae0_row194_col0, #T_87ae0_row194_col1, #T_87ae0_row194_col2, #T_87ae0_row194_col3, #T_87ae0_row194_col4, #T_87ae0_row194_col5, #T_87ae0_row194_col6 {\n",
               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_91c18\">\n",
+              "<table id=\"T_87ae0\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_91c18_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_91c18_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_91c18_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_91c18_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_91c18_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\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",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_91c18_row0_col0\" class=\"data row0 col0\" >validmind.prompt_validation.Bias</td>\n",
-              "      <td id=\"T_91c18_row0_col1\" class=\"data row0 col1\" >Bias</td>\n",
-              "      <td id=\"T_91c18_row0_col2\" class=\"data row0 col2\" >Evaluates bias in a Large Language Model based on the order and distribution of exemplars in a prompt....</td>\n",
-              "      <td id=\"T_91c18_row0_col3\" class=\"data row0 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row0_col4\" class=\"data row0 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row1_col0\" class=\"data row1 col0\" >validmind.prompt_validation.Clarity</td>\n",
-              "      <td id=\"T_91c18_row1_col1\" class=\"data row1 col1\" >Clarity</td>\n",
-              "      <td id=\"T_91c18_row1_col2\" class=\"data row1 col2\" >Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....</td>\n",
-              "      <td id=\"T_91c18_row1_col3\" class=\"data row1 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row1_col4\" class=\"data row1 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row2_col0\" class=\"data row2 col0\" >validmind.prompt_validation.Specificity</td>\n",
-              "      <td id=\"T_91c18_row2_col1\" class=\"data row2 col1\" >Specificity</td>\n",
-              "      <td id=\"T_91c18_row2_col2\" class=\"data row2 col2\" >Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity,...</td>\n",
-              "      <td id=\"T_91c18_row2_col3\" class=\"data row2 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row2_col4\" class=\"data row2 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row3_col0\" class=\"data row3 col0\" >validmind.prompt_validation.Robustness</td>\n",
-              "      <td id=\"T_91c18_row3_col1\" class=\"data row3 col1\" >Robustness</td>\n",
-              "      <td id=\"T_91c18_row3_col2\" class=\"data row3 col2\" >Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts....</td>\n",
-              "      <td id=\"T_91c18_row3_col3\" class=\"data row3 col3\" >['model']</td>\n",
-              "      <td id=\"T_91c18_row3_col4\" class=\"data row3 col4\" >{'num_tests': 10}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row4_col0\" class=\"data row4 col0\" >validmind.prompt_validation.NegativeInstruction</td>\n",
-              "      <td id=\"T_91c18_row4_col1\" class=\"data row4 col1\" >Negative Instruction</td>\n",
-              "      <td id=\"T_91c18_row4_col2\" class=\"data row4 col2\" >Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....</td>\n",
-              "      <td id=\"T_91c18_row4_col3\" class=\"data row4 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row4_col4\" class=\"data row4 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row5_col0\" class=\"data row5 col0\" >validmind.prompt_validation.Conciseness</td>\n",
-              "      <td id=\"T_91c18_row5_col1\" class=\"data row5 col1\" >Conciseness</td>\n",
-              "      <td id=\"T_91c18_row5_col2\" class=\"data row5 col2\" >Analyzes and grades the conciseness of prompts provided to a Large Language Model....</td>\n",
-              "      <td id=\"T_91c18_row5_col3\" class=\"data row5 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row5_col4\" class=\"data row5 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row6_col0\" class=\"data row6 col0\" >validmind.prompt_validation.Delimitation</td>\n",
-              "      <td id=\"T_91c18_row6_col1\" class=\"data row6 col1\" >Delimitation</td>\n",
-              "      <td id=\"T_91c18_row6_col2\" class=\"data row6 col2\" >Evaluates the proper use of delimiters in prompts provided to Large Language Models....</td>\n",
-              "      <td id=\"T_91c18_row6_col3\" class=\"data row6 col3\" >['model.prompt']</td>\n",
-              "      <td id=\"T_91c18_row6_col4\" class=\"data row6 col4\" >{'min_threshold': 7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.ModelPredictionResiduals</td>\n",
-              "      <td id=\"T_91c18_row7_col1\" class=\"data row7 col1\" >Model Prediction Residuals</td>\n",
-              "      <td id=\"T_91c18_row7_col2\" class=\"data row7 col2\" >Plot the residuals and histograms for each model, and generate a summary table...</td>\n",
-              "      <td id=\"T_91c18_row7_col3\" class=\"data row7 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row7_col4\" class=\"data row7 col4\" >{'nbins': 100, 'p_value_threshold': 0.05, 'start_date': None, 'end_date': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.BertScore</td>\n",
-              "      <td id=\"T_91c18_row8_col1\" class=\"data row8 col1\" >Bert Score</td>\n",
-              "      <td id=\"T_91c18_row8_col2\" class=\"data row8 col2\" >Evaluates the quality of machine-generated text using BERTScore metrics and visualizes the results through histograms...</td>\n",
-              "      <td id=\"T_91c18_row8_col3\" class=\"data row8 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row8_col4\" class=\"data row8 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.TimeSeriesPredictionsPlot</td>\n",
-              "      <td id=\"T_91c18_row9_col1\" class=\"data row9 col1\" >Time Series Predictions Plot</td>\n",
-              "      <td id=\"T_91c18_row9_col2\" class=\"data row9 col2\" >Plot actual vs predicted values for time series data and generate a visual comparison for each model....</td>\n",
-              "      <td id=\"T_91c18_row9_col3\" class=\"data row9 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row9_col4\" class=\"data row9 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.RegardScore</td>\n",
-              "      <td id=\"T_91c18_row10_col1\" class=\"data row10 col1\" >Regard Score</td>\n",
-              "      <td id=\"T_91c18_row10_col2\" class=\"data row10 col2\" >Computes and visualizes the regard score for each text instance, assessing sentiment and potential biases....</td>\n",
-              "      <td id=\"T_91c18_row10_col3\" class=\"data row10 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row10_col4\" class=\"data row10 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.BleuScore</td>\n",
-              "      <td id=\"T_91c18_row11_col1\" class=\"data row11 col1\" >Bleu Score</td>\n",
-              "      <td id=\"T_91c18_row11_col2\" class=\"data row11 col2\" >Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...</td>\n",
-              "      <td id=\"T_91c18_row11_col3\" class=\"data row11 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row11_col4\" class=\"data row11 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.TimeSeriesPredictionWithCI</td>\n",
-              "      <td id=\"T_91c18_row12_col1\" class=\"data row12 col1\" >Time Series Prediction With CI</td>\n",
-              "      <td id=\"T_91c18_row12_col2\" class=\"data row12 col2\" >Plot actual vs predicted values for a time series with confidence intervals and compute breaches....</td>\n",
-              "      <td id=\"T_91c18_row12_col3\" class=\"data row12 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row12_col4\" class=\"data row12 col4\" >{'confidence': 0.95}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.RegressionResidualsPlot</td>\n",
-              "      <td id=\"T_91c18_row13_col1\" class=\"data row13 col1\" >Regression Residuals Plot</td>\n",
-              "      <td id=\"T_91c18_row13_col2\" class=\"data row13 col2\" >Evaluates regression model performance using residual distribution and actual vs. predicted plots....</td>\n",
-              "      <td id=\"T_91c18_row13_col3\" class=\"data row13 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row13_col4\" class=\"data row13 col4\" >{'bin_size': 0.1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
-              "      <td id=\"T_91c18_row14_col1\" class=\"data row14 col1\" >Features AUC</td>\n",
-              "      <td id=\"T_91c18_row14_col2\" class=\"data row14 col2\" >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....</td>\n",
-              "      <td id=\"T_91c18_row14_col3\" class=\"data row14 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row14_col4\" class=\"data row14 col4\" >{'fontsize': 12, 'figure_height': 500}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.ContextualRecall</td>\n",
-              "      <td id=\"T_91c18_row15_col1\" class=\"data row15 col1\" >Contextual Recall</td>\n",
-              "      <td id=\"T_91c18_row15_col2\" class=\"data row15 col2\" >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 descriptive statistics for contextual recall scores....</td>\n",
-              "      <td id=\"T_91c18_row15_col3\" class=\"data row15 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row15_col4\" class=\"data row15 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.MeteorScore</td>\n",
-              "      <td id=\"T_91c18_row16_col1\" class=\"data row16 col1\" >Meteor Score</td>\n",
-              "      <td id=\"T_91c18_row16_col2\" class=\"data row16 col2\" >Computes and visualizes the METEOR score for each text generation instance, assessing translation quality....</td>\n",
-              "      <td id=\"T_91c18_row16_col3\" class=\"data row16 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row16_col4\" class=\"data row16 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.RougeScore</td>\n",
-              "      <td id=\"T_91c18_row17_col1\" class=\"data row17 col1\" >Rouge Score</td>\n",
-              "      <td id=\"T_91c18_row17_col2\" class=\"data row17 col2\" >Evaluates the quality of machine-generated text using ROUGE metrics and visualizes the results through histograms...</td>\n",
-              "      <td id=\"T_91c18_row17_col3\" class=\"data row17 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row17_col4\" class=\"data row17 col4\" >{'metric': 'rouge-1'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.ModelMetadata</td>\n",
-              "      <td id=\"T_91c18_row18_col1\" class=\"data row18 col1\" >Model Metadata</td>\n",
-              "      <td id=\"T_91c18_row18_col2\" class=\"data row18 col2\" >Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis....</td>\n",
-              "      <td id=\"T_91c18_row18_col3\" class=\"data row18 col3\" >['model']</td>\n",
-              "      <td id=\"T_91c18_row18_col4\" class=\"data row18 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
-              "      <td id=\"T_91c18_row19_col1\" class=\"data row19 col1\" >Cluster Size Distribution</td>\n",
-              "      <td id=\"T_91c18_row19_col2\" class=\"data row19 col2\" >Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing...</td>\n",
-              "      <td id=\"T_91c18_row19_col3\" class=\"data row19 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row19_col4\" class=\"data row19 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.TokenDisparity</td>\n",
-              "      <td id=\"T_91c18_row20_col1\" class=\"data row20 col1\" >Token Disparity</td>\n",
-              "      <td id=\"T_91c18_row20_col2\" class=\"data row20 col2\" >Evaluates the token disparity between reference and generated texts, visualizing the results through histograms...</td>\n",
-              "      <td id=\"T_91c18_row20_col3\" class=\"data row20 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row20_col4\" class=\"data row20 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.ToxicityScore</td>\n",
-              "      <td id=\"T_91c18_row21_col1\" class=\"data row21 col1\" >Toxicity Score</td>\n",
-              "      <td id=\"T_91c18_row21_col2\" class=\"data row21 col2\" >Computes and visualizes the toxicity score for input text, true text, and predicted text, assessing content quality and potential risk....</td>\n",
-              "      <td id=\"T_91c18_row21_col3\" class=\"data row21 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row21_col4\" class=\"data row21 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.ModelMetadataComparison</td>\n",
-              "      <td id=\"T_91c18_row22_col1\" class=\"data row22 col1\" >Model Metadata Comparison</td>\n",
-              "      <td id=\"T_91c18_row22_col2\" class=\"data row22 col2\" >Compare metadata of different models and generate a summary table with the results....</td>\n",
-              "      <td id=\"T_91c18_row22_col3\" class=\"data row22 col3\" >['models']</td>\n",
-              "      <td id=\"T_91c18_row22_col4\" class=\"data row22 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
-              "      <td id=\"T_91c18_row23_col1\" class=\"data row23 col1\" >Time Series R2 Square By Segments</td>\n",
-              "      <td id=\"T_91c18_row23_col2\" class=\"data row23 col2\" >Plot R-Squared values for each model over specified time segments and generate a bar chart...</td>\n",
-              "      <td id=\"T_91c18_row23_col3\" class=\"data row23 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row23_col4\" class=\"data row23 col4\" >{'segments': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.embeddings.CosineSimilarityComparison</td>\n",
-              "      <td id=\"T_91c18_row24_col1\" class=\"data row24 col1\" >Cosine Similarity Comparison</td>\n",
-              "      <td id=\"T_91c18_row24_col2\" class=\"data row24 col2\" >Computes pairwise cosine similarities between model embeddings and visualizes the results through bar charts,...</td>\n",
-              "      <td id=\"T_91c18_row24_col3\" class=\"data row24 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_91c18_row24_col4\" class=\"data row24 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.embeddings.EmbeddingsVisualization2D</td>\n",
-              "      <td id=\"T_91c18_row25_col1\" class=\"data row25 col1\" >Embeddings Visualization2 D</td>\n",
-              "      <td id=\"T_91c18_row25_col2\" class=\"data row25 col2\" >Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....</td>\n",
-              "      <td id=\"T_91c18_row25_col3\" class=\"data row25 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row25_col4\" class=\"data row25 col4\" >{'cluster_column': None, 'perplexity': 30}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.embeddings.StabilityAnalysisRandomNoise</td>\n",
-              "      <td id=\"T_91c18_row26_col1\" class=\"data row26 col1\" >Stability Analysis Random Noise</td>\n",
-              "      <td id=\"T_91c18_row26_col2\" class=\"data row26 col2\" >Evaluate robustness of embeddings models to random noise introduced by using...</td>\n",
-              "      <td id=\"T_91c18_row26_col3\" class=\"data row26 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row26_col4\" class=\"data row26 col4\" >{'mean_similarity_threshold': 0.7, 'probability': 0.02}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.embeddings.TSNEComponentsPairwisePlots</td>\n",
-              "      <td id=\"T_91c18_row27_col1\" class=\"data row27 col1\" >TSNE Components Pairwise Plots</td>\n",
-              "      <td id=\"T_91c18_row27_col2\" class=\"data row27 col2\" >Plots individual scatter plots for pairwise combinations of t-SNE components of embeddings....</td>\n",
-              "      <td id=\"T_91c18_row27_col3\" class=\"data row27 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row27_col4\" class=\"data row27 col4\" >{'n_components': 2, 'perplexity': 30, 'title': 't-SNE'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.embeddings.CosineSimilarityDistribution</td>\n",
-              "      <td id=\"T_91c18_row28_col1\" class=\"data row28 col1\" >Cosine Similarity Distribution</td>\n",
-              "      <td id=\"T_91c18_row28_col2\" class=\"data row28 col2\" >Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...</td>\n",
-              "      <td id=\"T_91c18_row28_col3\" class=\"data row28 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row28_col4\" class=\"data row28 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.embeddings.PCAComponentsPairwisePlots</td>\n",
-              "      <td id=\"T_91c18_row29_col1\" class=\"data row29 col1\" >PCA Components Pairwise Plots</td>\n",
-              "      <td id=\"T_91c18_row29_col2\" class=\"data row29 col2\" >Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model embeddings....</td>\n",
-              "      <td id=\"T_91c18_row29_col3\" class=\"data row29 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row29_col4\" class=\"data row29 col4\" >{'n_components': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.embeddings.CosineSimilarityHeatmap</td>\n",
-              "      <td id=\"T_91c18_row30_col1\" class=\"data row30 col1\" >Cosine Similarity Heatmap</td>\n",
-              "      <td id=\"T_91c18_row30_col2\" class=\"data row30 col2\" >Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....</td>\n",
-              "      <td id=\"T_91c18_row30_col3\" class=\"data row30 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row30_col4\" class=\"data row30 col4\" >{'title': 'Cosine Similarity Matrix', 'color': 'Cosine Similarity', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.embeddings.StabilityAnalysisTranslation</td>\n",
-              "      <td id=\"T_91c18_row31_col1\" class=\"data row31 col1\" >Stability Analysis Translation</td>\n",
-              "      <td id=\"T_91c18_row31_col2\" class=\"data row31 col2\" >Evaluate robustness of embeddings models to noise introduced by translating...</td>\n",
-              "      <td id=\"T_91c18_row31_col3\" class=\"data row31 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row31_col4\" class=\"data row31 col4\" >{'source_lang': 'en', 'target_lang': 'fr', 'mean_similarity_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.embeddings.EuclideanDistanceComparison</td>\n",
-              "      <td id=\"T_91c18_row32_col1\" class=\"data row32 col1\" >Euclidean Distance Comparison</td>\n",
-              "      <td id=\"T_91c18_row32_col2\" class=\"data row32 col2\" >Computes pairwise Euclidean distances between model embeddings and visualizes the results through bar charts,...</td>\n",
-              "      <td id=\"T_91c18_row32_col3\" class=\"data row32 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_91c18_row32_col4\" class=\"data row32 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.embeddings.ClusterDistribution</td>\n",
-              "      <td id=\"T_91c18_row33_col1\" class=\"data row33 col1\" >Cluster Distribution</td>\n",
-              "      <td id=\"T_91c18_row33_col2\" class=\"data row33 col2\" >Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....</td>\n",
-              "      <td id=\"T_91c18_row33_col3\" class=\"data row33 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row33_col4\" class=\"data row33 col4\" >{'num_clusters': 5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.embeddings.EuclideanDistanceHeatmap</td>\n",
-              "      <td id=\"T_91c18_row34_col1\" class=\"data row34 col1\" >Euclidean Distance Heatmap</td>\n",
-              "      <td id=\"T_91c18_row34_col2\" class=\"data row34 col2\" >Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....</td>\n",
-              "      <td id=\"T_91c18_row34_col3\" class=\"data row34 col3\" >['dataset', 'model']</td>\n",
-              "      <td id=\"T_91c18_row34_col4\" class=\"data row34 col4\" >{'title': 'Euclidean Distance Matrix', 'color': 'Euclidean Distance', 'xaxis_title': 'Index', 'yaxis_title': 'Index', 'color_scale': 'Blues'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row35_col0\" class=\"data row35 col0\" >validmind.model_validation.embeddings.StabilityAnalysis</td>\n",
-              "      <td id=\"T_91c18_row35_col1\" class=\"data row35 col1\" >Stability Analysis</td>\n",
-              "      <td id=\"T_91c18_row35_col2\" class=\"data row35 col2\" >Base class for embeddings stability analysis tests</td>\n",
-              "      <td id=\"T_91c18_row35_col3\" class=\"data row35 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row35_col4\" class=\"data row35 col4\" >{'mean_similarity_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row36_col0\" class=\"data row36 col0\" >validmind.model_validation.embeddings.StabilityAnalysisKeyword</td>\n",
-              "      <td id=\"T_91c18_row36_col1\" class=\"data row36 col1\" >Stability Analysis Keyword</td>\n",
-              "      <td id=\"T_91c18_row36_col2\" class=\"data row36 col2\" >Evaluate robustness of embeddings models to keyword swaps on the test dataset...</td>\n",
-              "      <td id=\"T_91c18_row36_col3\" class=\"data row36 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row36_col4\" class=\"data row36 col4\" >{'keyword_dict': None, 'mean_similarity_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row37_col0\" class=\"data row37 col0\" >validmind.model_validation.embeddings.StabilityAnalysisSynonyms</td>\n",
-              "      <td id=\"T_91c18_row37_col1\" class=\"data row37 col1\" >Stability Analysis Synonyms</td>\n",
-              "      <td id=\"T_91c18_row37_col2\" class=\"data row37 col2\" >Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....</td>\n",
-              "      <td id=\"T_91c18_row37_col3\" class=\"data row37 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row37_col4\" class=\"data row37 col4\" >{'probability': 0.02, 'mean_similarity_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row38_col0\" class=\"data row38 col0\" >validmind.model_validation.embeddings.DescriptiveAnalytics</td>\n",
-              "      <td id=\"T_91c18_row38_col1\" class=\"data row38 col1\" >Descriptive Analytics</td>\n",
-              "      <td id=\"T_91c18_row38_col2\" class=\"data row38 col2\" >Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...</td>\n",
-              "      <td id=\"T_91c18_row38_col3\" class=\"data row38 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row38_col4\" class=\"data row38 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row39_col0\" class=\"data row39 col0\" >validmind.model_validation.ragas.ContextEntityRecall</td>\n",
-              "      <td id=\"T_91c18_row39_col1\" class=\"data row39 col1\" >Context Entity Recall</td>\n",
-              "      <td id=\"T_91c18_row39_col2\" class=\"data row39 col2\" >Evaluates the context entity recall for dataset entries and visualizes the results....</td>\n",
-              "      <td id=\"T_91c18_row39_col3\" class=\"data row39 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row39_col4\" class=\"data row39 col4\" >{'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row40_col0\" class=\"data row40 col0\" >validmind.model_validation.ragas.Faithfulness</td>\n",
-              "      <td id=\"T_91c18_row40_col1\" class=\"data row40 col1\" >Faithfulness</td>\n",
-              "      <td id=\"T_91c18_row40_col2\" class=\"data row40 col2\" >Evaluates the faithfulness of the generated answers with respect to retrieved contexts....</td>\n",
-              "      <td id=\"T_91c18_row40_col3\" class=\"data row40 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row40_col4\" class=\"data row40 col4\" >{'answer_column': 'answer', 'contexts_column': 'contexts'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row41_col0\" class=\"data row41 col0\" >validmind.model_validation.ragas.AspectCritique</td>\n",
-              "      <td id=\"T_91c18_row41_col1\" class=\"data row41 col1\" >Aspect Critique</td>\n",
-              "      <td id=\"T_91c18_row41_col2\" class=\"data row41 col2\" >Evaluates generations against the following aspects: harmfulness, maliciousness,...</td>\n",
-              "      <td id=\"T_91c18_row41_col3\" class=\"data row41 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row41_col4\" class=\"data row41 col4\" >{'question_column': 'question', 'answer_column': 'answer', 'contexts_column': 'contexts', 'aspects': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness'], 'additional_aspects': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row42_col0\" class=\"data row42 col0\" >validmind.model_validation.ragas.AnswerSimilarity</td>\n",
-              "      <td id=\"T_91c18_row42_col1\" class=\"data row42 col1\" >Answer Similarity</td>\n",
-              "      <td id=\"T_91c18_row42_col2\" class=\"data row42 col2\" >Calculates the semantic similarity between generated answers and ground truths...</td>\n",
-              "      <td id=\"T_91c18_row42_col3\" class=\"data row42 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row42_col4\" class=\"data row42 col4\" >{'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row43_col0\" class=\"data row43 col0\" >validmind.model_validation.ragas.AnswerCorrectness</td>\n",
-              "      <td id=\"T_91c18_row43_col1\" class=\"data row43 col1\" >Answer Correctness</td>\n",
-              "      <td id=\"T_91c18_row43_col2\" class=\"data row43 col2\" >Evaluates the correctness of answers in a dataset with respect to the provided ground...</td>\n",
-              "      <td id=\"T_91c18_row43_col3\" class=\"data row43 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row43_col4\" class=\"data row43 col4\" >{'question_column': 'question', 'answer_column': 'answer', 'ground_truth_column': 'ground_truth'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row44_col0\" class=\"data row44 col0\" >validmind.model_validation.ragas.ContextRecall</td>\n",
-              "      <td id=\"T_91c18_row44_col1\" class=\"data row44 col1\" >Context Recall</td>\n",
-              "      <td id=\"T_91c18_row44_col2\" class=\"data row44 col2\" >Context recall measures the extent to which the retrieved context aligns with the...</td>\n",
-              "      <td id=\"T_91c18_row44_col3\" class=\"data row44 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row44_col4\" class=\"data row44 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row45_col0\" class=\"data row45 col0\" >validmind.model_validation.ragas.ContextRelevancy</td>\n",
-              "      <td id=\"T_91c18_row45_col1\" class=\"data row45 col1\" >Context Relevancy</td>\n",
-              "      <td id=\"T_91c18_row45_col2\" class=\"data row45 col2\" >Evaluates the context relevancy metric for entries in a dataset and visualizes the...</td>\n",
-              "      <td id=\"T_91c18_row45_col3\" class=\"data row45 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row45_col4\" class=\"data row45 col4\" >{'question_column': 'question', 'contexts_column': 'contexts'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row46_col0\" class=\"data row46 col0\" >validmind.model_validation.ragas.ContextPrecision</td>\n",
-              "      <td id=\"T_91c18_row46_col1\" class=\"data row46 col1\" >Context Precision</td>\n",
-              "      <td id=\"T_91c18_row46_col2\" class=\"data row46 col2\" >Context Precision is a metric that evaluates whether all of the ground-truth...</td>\n",
-              "      <td id=\"T_91c18_row46_col3\" class=\"data row46 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row46_col4\" class=\"data row46 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'ground_truth_column': 'ground_truth'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row47_col0\" class=\"data row47 col0\" >validmind.model_validation.ragas.AnswerRelevance</td>\n",
-              "      <td id=\"T_91c18_row47_col1\" class=\"data row47 col1\" >Answer Relevance</td>\n",
-              "      <td id=\"T_91c18_row47_col2\" class=\"data row47 col2\" >Assesses how pertinent the generated answer is to the given prompt....</td>\n",
-              "      <td id=\"T_91c18_row47_col3\" class=\"data row47 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row47_col4\" class=\"data row47 col4\" >{'question_column': 'question', 'contexts_column': 'contexts', 'answer_column': 'answer'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row48_col0\" class=\"data row48 col0\" >validmind.model_validation.sklearn.RegressionModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_91c18_row48_col1\" class=\"data row48 col1\" >Regression Models Performance Comparison</td>\n",
-              "      <td id=\"T_91c18_row48_col2\" class=\"data row48 col2\" >Compares and evaluates the performance of multiple regression models using five different metrics: MAE, MSE, RMSE,...</td>\n",
-              "      <td id=\"T_91c18_row48_col3\" class=\"data row48 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_91c18_row48_col4\" class=\"data row48 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row49_col0\" class=\"data row49 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
-              "      <td id=\"T_91c18_row49_col1\" class=\"data row49 col1\" >Adjusted Mutual Information</td>\n",
-              "      <td id=\"T_91c18_row49_col2\" class=\"data row49 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
-              "      <td id=\"T_91c18_row49_col3\" class=\"data row49 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row49_col4\" class=\"data row49 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row50_col0\" class=\"data row50 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
-              "      <td id=\"T_91c18_row50_col1\" class=\"data row50 col1\" >Silhouette Plot</td>\n",
-              "      <td id=\"T_91c18_row50_col2\" class=\"data row50 col2\" >Calculates and visualizes Silhouette Score, assessing degree of data point suitability to its cluster in ML models....</td>\n",
-              "      <td id=\"T_91c18_row50_col3\" class=\"data row50 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row50_col4\" class=\"data row50 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row51_col0\" class=\"data row51 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_91c18_row51_col1\" class=\"data row51 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_91c18_row51_col2\" class=\"data row51 col2\" >Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...</td>\n",
-              "      <td id=\"T_91c18_row51_col3\" class=\"data row51 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row51_col4\" class=\"data row51 col4\" >{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row52_col0\" class=\"data row52 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
-              "      <td id=\"T_91c18_row52_col1\" class=\"data row52 col1\" >Adjusted Rand Index</td>\n",
-              "      <td id=\"T_91c18_row52_col2\" class=\"data row52 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
-              "      <td id=\"T_91c18_row52_col3\" class=\"data row52 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row52_col4\" class=\"data row52 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row53_col0\" class=\"data row53 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_91c18_row53_col1\" class=\"data row53 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_91c18_row53_col2\" class=\"data row53 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_91c18_row53_col3\" class=\"data row53 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row53_col4\" class=\"data row53 col4\" >{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row54_col0\" class=\"data row54 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_91c18_row54_col1\" class=\"data row54 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_91c18_row54_col2\" class=\"data row54 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_91c18_row54_col3\" class=\"data row54 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row54_col4\" class=\"data row54 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row55_col0\" class=\"data row55 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
-              "      <td id=\"T_91c18_row55_col1\" class=\"data row55 col1\" >Homogeneity Score</td>\n",
-              "      <td id=\"T_91c18_row55_col2\" class=\"data row55 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
-              "      <td id=\"T_91c18_row55_col3\" class=\"data row55 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row55_col4\" class=\"data row55 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row56_col0\" class=\"data row56 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
-              "      <td id=\"T_91c18_row56_col1\" class=\"data row56 col1\" >Completeness Score</td>\n",
-              "      <td id=\"T_91c18_row56_col2\" class=\"data row56 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
-              "      <td id=\"T_91c18_row56_col3\" class=\"data row56 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row56_col4\" class=\"data row56 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row57_col0\" class=\"data row57 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_91c18_row57_col1\" class=\"data row57 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_91c18_row57_col2\" class=\"data row57 col2\" >Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....</td>\n",
-              "      <td id=\"T_91c18_row57_col3\" class=\"data row57 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row57_col4\" class=\"data row57 col4\" >{'features_columns': None, 'cut_off_percentage': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row58_col0\" class=\"data row58 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
-              "      <td id=\"T_91c18_row58_col1\" class=\"data row58 col1\" >Cluster Performance Metrics</td>\n",
-              "      <td id=\"T_91c18_row58_col2\" class=\"data row58 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
-              "      <td id=\"T_91c18_row58_col3\" class=\"data row58 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row58_col4\" class=\"data row58 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row59_col0\" class=\"data row59 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_91c18_row59_col1\" class=\"data row59 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_91c18_row59_col2\" class=\"data row59 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_91c18_row59_col3\" class=\"data row59 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row59_col4\" class=\"data row59 col4\" >{'fontsize': None, 'figure_height': 1000}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row60_col0\" class=\"data row60 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
-              "      <td id=\"T_91c18_row60_col1\" class=\"data row60 col1\" >Fowlkes Mallows Score</td>\n",
-              "      <td id=\"T_91c18_row60_col2\" class=\"data row60 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
-              "      <td id=\"T_91c18_row60_col3\" class=\"data row60 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row60_col4\" class=\"data row60 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row61_col0\" class=\"data row61 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_91c18_row61_col1\" class=\"data row61 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_91c18_row61_col2\" class=\"data row61 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_91c18_row61_col3\" class=\"data row61 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row61_col4\" class=\"data row61 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row62_col0\" class=\"data row62 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
-              "      <td id=\"T_91c18_row62_col1\" class=\"data row62 col1\" >Cluster Cosine Similarity</td>\n",
-              "      <td id=\"T_91c18_row62_col2\" class=\"data row62 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
-              "      <td id=\"T_91c18_row62_col3\" class=\"data row62 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row62_col4\" class=\"data row62 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row63_col0\" class=\"data row63 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_91c18_row63_col1\" class=\"data row63 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_91c18_row63_col2\" class=\"data row63 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_91c18_row63_col3\" class=\"data row63 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row63_col4\" class=\"data row63 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row64_col0\" class=\"data row64 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_91c18_row64_col1\" class=\"data row64 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_91c18_row64_col2\" class=\"data row64 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_91c18_row64_col3\" class=\"data row64 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row64_col4\" class=\"data row64 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row65_col0\" class=\"data row65 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
-              "      <td id=\"T_91c18_row65_col1\" class=\"data row65 col1\" >V Measure</td>\n",
-              "      <td id=\"T_91c18_row65_col2\" class=\"data row65 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
-              "      <td id=\"T_91c18_row65_col3\" class=\"data row65 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row65_col4\" class=\"data row65 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row66_col0\" class=\"data row66 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_91c18_row66_col1\" class=\"data row66 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_91c18_row66_col2\" class=\"data row66 col2\" >Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....</td>\n",
-              "      <td id=\"T_91c18_row66_col3\" class=\"data row66 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row66_col4\" class=\"data row66 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row67_col0\" class=\"data row67 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_91c18_row67_col1\" class=\"data row67 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_91c18_row67_col2\" class=\"data row67 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_91c18_row67_col3\" class=\"data row67 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row67_col4\" class=\"data row67 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row68_col0\" class=\"data row68 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
-              "      <td id=\"T_91c18_row68_col1\" class=\"data row68 col1\" >Regression R2 Square</td>\n",
-              "      <td id=\"T_91c18_row68_col2\" class=\"data row68 col2\" >**Purpose**: The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a...</td>\n",
-              "      <td id=\"T_91c18_row68_col3\" class=\"data row68 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row68_col4\" class=\"data row68 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row69_col0\" class=\"data row69 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
-              "      <td id=\"T_91c18_row69_col1\" class=\"data row69 col1\" >Regression Errors</td>\n",
-              "      <td id=\"T_91c18_row69_col2\" class=\"data row69 col2\" >**Purpose**: This metric is used to measure the performance of a regression model. It gauges the model's accuracy...</td>\n",
-              "      <td id=\"T_91c18_row69_col3\" class=\"data row69 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row69_col4\" class=\"data row69 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row70_col0\" class=\"data row70 col0\" >validmind.model_validation.sklearn.ClusterPerformance</td>\n",
-              "      <td id=\"T_91c18_row70_col1\" class=\"data row70 col1\" >Cluster Performance</td>\n",
-              "      <td id=\"T_91c18_row70_col2\" class=\"data row70 col2\" >Evaluates and compares a clustering model's performance on training and testing datasets using multiple defined...</td>\n",
-              "      <td id=\"T_91c18_row70_col3\" class=\"data row70 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row70_col4\" class=\"data row70 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row71_col0\" class=\"data row71 col0\" >validmind.model_validation.sklearn.FeatureImportanceComparison</td>\n",
-              "      <td id=\"T_91c18_row71_col1\" class=\"data row71 col1\" >Feature Importance Comparison</td>\n",
-              "      <td id=\"T_91c18_row71_col2\" class=\"data row71 col2\" >Compare feature importance scores for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_91c18_row71_col3\" class=\"data row71 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row71_col4\" class=\"data row71 col4\" >{'num_features': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row72_col0\" class=\"data row72 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_91c18_row72_col1\" class=\"data row72 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_91c18_row72_col2\" class=\"data row72 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_91c18_row72_col3\" class=\"data row72 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row72_col4\" class=\"data row72 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row73_col0\" class=\"data row73 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
-              "      <td id=\"T_91c18_row73_col1\" class=\"data row73 col1\" >Regression Errors Comparison</td>\n",
-              "      <td id=\"T_91c18_row73_col2\" class=\"data row73 col2\" >Compare regression error metrics for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_91c18_row73_col3\" class=\"data row73 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row73_col4\" class=\"data row73 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row74_col0\" class=\"data row74 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_91c18_row74_col1\" class=\"data row74 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_91c18_row74_col2\" class=\"data row74 col2\" >Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....</td>\n",
-              "      <td id=\"T_91c18_row74_col3\" class=\"data row74 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row74_col4\" class=\"data row74 col4\" >{'param_grid': None, 'scoring': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row75_col0\" class=\"data row75 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
-              "      <td id=\"T_91c18_row75_col1\" class=\"data row75 col1\" >K Means Clusters Optimization</td>\n",
-              "      <td id=\"T_91c18_row75_col2\" class=\"data row75 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
-              "      <td id=\"T_91c18_row75_col3\" class=\"data row75 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row75_col4\" class=\"data row75 col4\" >{'n_clusters': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row76_col0\" class=\"data row76 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_91c18_row76_col1\" class=\"data row76 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_91c18_row76_col2\" class=\"data row76 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_91c18_row76_col3\" class=\"data row76 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_91c18_row76_col4\" class=\"data row76 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row77_col0\" class=\"data row77 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_91c18_row77_col1\" class=\"data row77 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_91c18_row77_col2\" class=\"data row77 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_91c18_row77_col3\" class=\"data row77 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row77_col4\" class=\"data row77 col4\" >{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row78_col0\" class=\"data row78 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
-              "      <td id=\"T_91c18_row78_col1\" class=\"data row78 col1\" >Regression R2 Square Comparison</td>\n",
-              "      <td id=\"T_91c18_row78_col2\" class=\"data row78 col2\" >Compare R-Squared and Adjusted R-Squared values for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_91c18_row78_col3\" class=\"data row78 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_91c18_row78_col4\" class=\"data row78 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row79_col0\" class=\"data row79 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_91c18_row79_col1\" class=\"data row79 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_91c18_row79_col2\" class=\"data row79 col2\" >Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_91c18_row79_col3\" class=\"data row79 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row79_col4\" class=\"data row79 col4\" >{'num_bins': 10, 'mode': 'fixed'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row80_col0\" class=\"data row80 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_91c18_row80_col1\" class=\"data row80 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_91c18_row80_col2\" class=\"data row80 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_91c18_row80_col3\" class=\"data row80 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row80_col4\" class=\"data row80 col4\" >{'min_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row81_col0\" class=\"data row81 col0\" >validmind.model_validation.statsmodels.RegressionModelsCoeffs</td>\n",
-              "      <td id=\"T_91c18_row81_col1\" class=\"data row81 col1\" >Regression Models Coeffs</td>\n",
-              "      <td id=\"T_91c18_row81_col2\" class=\"data row81 col2\" >Compares feature importance by evaluating and contrasting coefficients of different regression models....</td>\n",
-              "      <td id=\"T_91c18_row81_col3\" class=\"data row81 col3\" >['models']</td>\n",
-              "      <td id=\"T_91c18_row81_col4\" class=\"data row81 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row82_col0\" class=\"data row82 col0\" >validmind.model_validation.statsmodels.BoxPierce</td>\n",
-              "      <td id=\"T_91c18_row82_col1\" class=\"data row82 col1\" >Box Pierce</td>\n",
-              "      <td id=\"T_91c18_row82_col2\" class=\"data row82 col2\" >Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....</td>\n",
-              "      <td id=\"T_91c18_row82_col3\" class=\"data row82 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row82_col4\" class=\"data row82 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row83_col0\" class=\"data row83 col0\" >validmind.model_validation.statsmodels.RegressionCoeffsPlot</td>\n",
-              "      <td id=\"T_91c18_row83_col1\" class=\"data row83 col1\" >Regression Coeffs Plot</td>\n",
-              "      <td id=\"T_91c18_row83_col2\" class=\"data row83 col2\" >Visualizes regression coefficients with 95% confidence intervals to assess predictor variables' impact on response...</td>\n",
-              "      <td id=\"T_91c18_row83_col3\" class=\"data row83 col3\" >['models']</td>\n",
-              "      <td id=\"T_91c18_row83_col4\" class=\"data row83 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row84_col0\" class=\"data row84 col0\" >validmind.model_validation.statsmodels.RegressionModelSensitivityPlot</td>\n",
-              "      <td id=\"T_91c18_row84_col1\" class=\"data row84 col1\" >Regression Model Sensitivity Plot</td>\n",
-              "      <td id=\"T_91c18_row84_col2\" class=\"data row84 col2\" >Tests the sensitivity of a regression model to variations in independent variables by applying shocks and...</td>\n",
-              "      <td id=\"T_91c18_row84_col3\" class=\"data row84 col3\" >['models', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row84_col4\" class=\"data row84 col4\" >{'transformation': None, 'shocks': [0.1]}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row85_col0\" class=\"data row85 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels</td>\n",
-              "      <td id=\"T_91c18_row85_col1\" class=\"data row85 col1\" >Regression Model Forecast Plot Levels</td>\n",
-              "      <td id=\"T_91c18_row85_col2\" class=\"data row85 col2\" >Compares and visualizes forecasted and actual values of regression models on both raw and transformed datasets....</td>\n",
-              "      <td id=\"T_91c18_row85_col3\" class=\"data row85 col3\" >['models', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row85_col4\" class=\"data row85 col4\" >{'transformation': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row86_col0\" class=\"data row86 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
-              "      <td id=\"T_91c18_row86_col1\" class=\"data row86 col1\" >Scorecard Histogram</td>\n",
-              "      <td id=\"T_91c18_row86_col2\" class=\"data row86 col2\" >Creates histograms of credit scores, from both default and non-default instances, generated by a credit-risk model....</td>\n",
-              "      <td id=\"T_91c18_row86_col3\" class=\"data row86 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_91c18_row86_col4\" class=\"data row86 col4\" >{'title': 'Histogram of Scores', 'score_column': 'score'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row87_col0\" class=\"data row87 col0\" >validmind.model_validation.statsmodels.LJungBox</td>\n",
-              "      <td id=\"T_91c18_row87_col1\" class=\"data row87 col1\" >L Jung Box</td>\n",
-              "      <td id=\"T_91c18_row87_col2\" class=\"data row87 col2\" >Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....</td>\n",
-              "      <td id=\"T_91c18_row87_col3\" class=\"data row87 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row87_col4\" class=\"data row87 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row88_col0\" class=\"data row88 col0\" >validmind.model_validation.statsmodels.JarqueBera</td>\n",
-              "      <td id=\"T_91c18_row88_col1\" class=\"data row88 col1\" >Jarque Bera</td>\n",
-              "      <td id=\"T_91c18_row88_col2\" class=\"data row88 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
-              "      <td id=\"T_91c18_row88_col3\" class=\"data row88 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row88_col4\" class=\"data row88 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row89_col0\" class=\"data row89 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
-              "      <td id=\"T_91c18_row89_col1\" class=\"data row89 col1\" >Kolmogorov Smirnov</td>\n",
-              "      <td id=\"T_91c18_row89_col2\" class=\"data row89 col2\" >Executes a feature-wise Kolmogorov-Smirnov test to evaluate alignment with normal distribution in datasets....</td>\n",
-              "      <td id=\"T_91c18_row89_col3\" class=\"data row89 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row89_col4\" class=\"data row89 col4\" >{'dist': 'norm'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row90_col0\" class=\"data row90 col0\" >validmind.model_validation.statsmodels.ShapiroWilk</td>\n",
-              "      <td id=\"T_91c18_row90_col1\" class=\"data row90 col1\" >Shapiro Wilk</td>\n",
-              "      <td id=\"T_91c18_row90_col2\" class=\"data row90 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
-              "      <td id=\"T_91c18_row90_col3\" class=\"data row90 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row90_col4\" class=\"data row90 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row91_col0\" class=\"data row91 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
-              "      <td id=\"T_91c18_row91_col1\" class=\"data row91 col1\" >Cumulative Prediction Probabilities</td>\n",
-              "      <td id=\"T_91c18_row91_col2\" class=\"data row91 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in logistic...</td>\n",
-              "      <td id=\"T_91c18_row91_col3\" class=\"data row91 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row91_col4\" class=\"data row91 col4\" >{'title': 'Cumulative Probabilities'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row92_col0\" class=\"data row92 col0\" >validmind.model_validation.statsmodels.RegressionFeatureSignificance</td>\n",
-              "      <td id=\"T_91c18_row92_col1\" class=\"data row92 col1\" >Regression Feature Significance</td>\n",
-              "      <td id=\"T_91c18_row92_col2\" class=\"data row92 col2\" >Assesses and visualizes the statistical significance of features in a set of regression models....</td>\n",
-              "      <td id=\"T_91c18_row92_col3\" class=\"data row92 col3\" >['models']</td>\n",
-              "      <td id=\"T_91c18_row92_col4\" class=\"data row92 col4\" >{'fontsize': 10, 'p_threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row93_col0\" class=\"data row93 col0\" >validmind.model_validation.statsmodels.RegressionModelSummary</td>\n",
-              "      <td id=\"T_91c18_row93_col1\" class=\"data row93 col1\" >Regression Model Summary</td>\n",
-              "      <td id=\"T_91c18_row93_col2\" class=\"data row93 col2\" >Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....</td>\n",
-              "      <td id=\"T_91c18_row93_col3\" class=\"data row93 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row93_col4\" class=\"data row93 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row94_col0\" class=\"data row94 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
-              "      <td id=\"T_91c18_row94_col1\" class=\"data row94 col1\" >Lilliefors</td>\n",
-              "      <td id=\"T_91c18_row94_col2\" class=\"data row94 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
-              "      <td id=\"T_91c18_row94_col3\" class=\"data row94 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row94_col4\" class=\"data row94 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row95_col0\" class=\"data row95 col0\" >validmind.model_validation.statsmodels.RunsTest</td>\n",
-              "      <td id=\"T_91c18_row95_col1\" class=\"data row95 col1\" >Runs Test</td>\n",
-              "      <td id=\"T_91c18_row95_col2\" class=\"data row95 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
-              "      <td id=\"T_91c18_row95_col3\" class=\"data row95 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row95_col4\" class=\"data row95 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row96_col0\" class=\"data row96 col0\" >validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance</td>\n",
-              "      <td id=\"T_91c18_row96_col1\" class=\"data row96 col1\" >Regression Permutation Feature Importance</td>\n",
-              "      <td id=\"T_91c18_row96_col2\" class=\"data row96 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_91c18_row96_col3\" class=\"data row96 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_91c18_row96_col4\" class=\"data row96 col4\" >{'fontsize': 12, 'figure_height': 500}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row97_col0\" class=\"data row97 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
-              "      <td id=\"T_91c18_row97_col1\" class=\"data row97 col1\" >Prediction Probabilities Histogram</td>\n",
-              "      <td id=\"T_91c18_row97_col2\" class=\"data row97 col2\" >Generates and visualizes histograms of the Probability of Default predictions for both positive and negative...</td>\n",
-              "      <td id=\"T_91c18_row97_col3\" class=\"data row97 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row97_col4\" class=\"data row97 col4\" >{'title': 'Histogram of Predictive Probabilities'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row98_col0\" class=\"data row98 col0\" >validmind.model_validation.statsmodels.AutoARIMA</td>\n",
-              "      <td id=\"T_91c18_row98_col1\" class=\"data row98 col1\" >Auto ARIMA</td>\n",
-              "      <td id=\"T_91c18_row98_col2\" class=\"data row98 col2\" >Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....</td>\n",
-              "      <td id=\"T_91c18_row98_col3\" class=\"data row98 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row98_col4\" class=\"data row98 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row99_col0\" class=\"data row99 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
-              "      <td id=\"T_91c18_row99_col1\" class=\"data row99 col1\" >GINI Table</td>\n",
-              "      <td id=\"T_91c18_row99_col2\" class=\"data row99 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
-              "      <td id=\"T_91c18_row99_col3\" class=\"data row99 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row99_col4\" class=\"data row99 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row100_col0\" class=\"data row100 col0\" >validmind.model_validation.statsmodels.RegressionModelForecastPlot</td>\n",
-              "      <td id=\"T_91c18_row100_col1\" class=\"data row100 col1\" >Regression Model Forecast Plot</td>\n",
-              "      <td id=\"T_91c18_row100_col2\" class=\"data row100 col2\" >Generates plots to visually compare the forecasted outcomes of one or more regression models against actual...</td>\n",
-              "      <td id=\"T_91c18_row100_col3\" class=\"data row100 col3\" >['models', 'datasets']</td>\n",
-              "      <td id=\"T_91c18_row100_col4\" class=\"data row100 col4\" >{'start_date': None, 'end_date': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row101_col0\" class=\"data row101 col0\" >validmind.model_validation.statsmodels.DurbinWatsonTest</td>\n",
-              "      <td id=\"T_91c18_row101_col1\" class=\"data row101 col1\" >Durbin Watson Test</td>\n",
-              "      <td id=\"T_91c18_row101_col2\" class=\"data row101 col2\" >Assesses autocorrelation in time series data features using the Durbin-Watson statistic....</td>\n",
-              "      <td id=\"T_91c18_row101_col3\" class=\"data row101 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row101_col4\" class=\"data row101 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row102_col0\" class=\"data row102 col0\" >validmind.data_validation.MissingValuesRisk</td>\n",
-              "      <td id=\"T_91c18_row102_col1\" class=\"data row102 col1\" >Missing Values Risk</td>\n",
-              "      <td id=\"T_91c18_row102_col2\" class=\"data row102 col2\" >Assesses and quantifies the risk related to missing values in a dataset used for training an ML model....</td>\n",
-              "      <td id=\"T_91c18_row102_col3\" class=\"data row102 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row102_col4\" class=\"data row102 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row103_col0\" class=\"data row103 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
-              "      <td id=\"T_91c18_row103_col1\" class=\"data row103 col1\" >IQR Outliers Table</td>\n",
-              "      <td id=\"T_91c18_row103_col2\" class=\"data row103 col2\" >Determines and summarizes outliers in numerical features using Interquartile Range method....</td>\n",
-              "      <td id=\"T_91c18_row103_col3\" class=\"data row103 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row103_col4\" class=\"data row103 col4\" >{'features': None, 'threshold': 1.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row104_col0\" class=\"data row104 col0\" >validmind.data_validation.BivariateFeaturesBarPlots</td>\n",
-              "      <td id=\"T_91c18_row104_col1\" class=\"data row104 col1\" >Bivariate Features Bar Plots</td>\n",
-              "      <td id=\"T_91c18_row104_col2\" class=\"data row104 col2\" >Generates visual bar plots to analyze the relationship between paired features within categorical data in the model....</td>\n",
-              "      <td id=\"T_91c18_row104_col3\" class=\"data row104 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row104_col4\" class=\"data row104 col4\" >{'features_pairs': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row105_col0\" class=\"data row105 col0\" >validmind.data_validation.Skewness</td>\n",
-              "      <td id=\"T_91c18_row105_col1\" class=\"data row105 col1\" >Skewness</td>\n",
-              "      <td id=\"T_91c18_row105_col2\" class=\"data row105 col2\" >Evaluates the skewness of numerical data in a machine learning model and checks if it falls below a set maximum...</td>\n",
-              "      <td id=\"T_91c18_row105_col3\" class=\"data row105 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row105_col4\" class=\"data row105 col4\" >{'max_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row106_col0\" class=\"data row106 col0\" >validmind.data_validation.Duplicates</td>\n",
-              "      <td id=\"T_91c18_row106_col1\" class=\"data row106 col1\" >Duplicates</td>\n",
-              "      <td id=\"T_91c18_row106_col2\" class=\"data row106 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
-              "      <td id=\"T_91c18_row106_col3\" class=\"data row106 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row106_col4\" class=\"data row106 col4\" >{'min_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row107_col0\" class=\"data row107 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
-              "      <td id=\"T_91c18_row107_col1\" class=\"data row107 col1\" >Missing Values Bar Plot</td>\n",
-              "      <td id=\"T_91c18_row107_col2\" class=\"data row107 col2\" >Creates a bar plot showcasing the percentage of missing values in each column of the dataset with risk...</td>\n",
-              "      <td id=\"T_91c18_row107_col3\" class=\"data row107 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row107_col4\" class=\"data row107 col4\" >{'threshold': 80, 'fig_height': 600}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row108_col0\" class=\"data row108 col0\" >validmind.data_validation.DatasetDescription</td>\n",
-              "      <td id=\"T_91c18_row108_col1\" class=\"data row108 col1\" >Dataset Description</td>\n",
-              "      <td id=\"T_91c18_row108_col2\" class=\"data row108 col2\" >Provides comprehensive analysis and statistical summaries of each field in a machine learning model's dataset....</td>\n",
-              "      <td id=\"T_91c18_row108_col3\" class=\"data row108 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row108_col4\" class=\"data row108 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row109_col0\" class=\"data row109 col0\" >validmind.data_validation.ZivotAndrewsArch</td>\n",
-              "      <td id=\"T_91c18_row109_col1\" class=\"data row109 col1\" >Zivot Andrews Arch</td>\n",
-              "      <td id=\"T_91c18_row109_col2\" class=\"data row109 col2\" >Evaluates the order of integration and stationarity of time series data using Zivot-Andrews unit root test....</td>\n",
-              "      <td id=\"T_91c18_row109_col3\" class=\"data row109 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row109_col4\" class=\"data row109 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row110_col0\" class=\"data row110 col0\" >validmind.data_validation.ScatterPlot</td>\n",
-              "      <td id=\"T_91c18_row110_col1\" class=\"data row110 col1\" >Scatter Plot</td>\n",
-              "      <td id=\"T_91c18_row110_col2\" class=\"data row110 col2\" >Creates a scatter plot matrix to visually analyze feature relationships, patterns, and outliers in a dataset....</td>\n",
-              "      <td id=\"T_91c18_row110_col3\" class=\"data row110 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row110_col4\" class=\"data row110 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row111_col0\" class=\"data row111 col0\" >validmind.data_validation.TimeSeriesOutliers</td>\n",
-              "      <td id=\"T_91c18_row111_col1\" class=\"data row111 col1\" >Time Series Outliers</td>\n",
-              "      <td id=\"T_91c18_row111_col2\" class=\"data row111 col2\" >Identifies and visualizes outliers in time-series data using z-score method....</td>\n",
-              "      <td id=\"T_91c18_row111_col3\" class=\"data row111 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row111_col4\" class=\"data row111 col4\" >{'zscore_threshold': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row112_col0\" class=\"data row112 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
-              "      <td id=\"T_91c18_row112_col1\" class=\"data row112 col1\" >Tabular Categorical Bar Plots</td>\n",
-              "      <td id=\"T_91c18_row112_col2\" class=\"data row112 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate dataset's composition....</td>\n",
-              "      <td id=\"T_91c18_row112_col3\" class=\"data row112 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row112_col4\" class=\"data row112 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row113_col0\" class=\"data row113 col0\" >validmind.data_validation.AutoStationarity</td>\n",
-              "      <td id=\"T_91c18_row113_col1\" class=\"data row113 col1\" >Auto Stationarity</td>\n",
-              "      <td id=\"T_91c18_row113_col2\" class=\"data row113 col2\" >Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....</td>\n",
-              "      <td id=\"T_91c18_row113_col3\" class=\"data row113 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row113_col4\" class=\"data row113 col4\" >{'max_order': 5, 'threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row114_col0\" class=\"data row114 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
-              "      <td id=\"T_91c18_row114_col1\" class=\"data row114 col1\" >Descriptive Statistics</td>\n",
-              "      <td id=\"T_91c18_row114_col2\" class=\"data row114 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
-              "      <td id=\"T_91c18_row114_col3\" class=\"data row114 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row114_col4\" class=\"data row114 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row115_col0\" class=\"data row115 col0\" >validmind.data_validation.TimeSeriesDescription</td>\n",
-              "      <td id=\"T_91c18_row115_col1\" class=\"data row115 col1\" >Time Series Description</td>\n",
-              "      <td id=\"T_91c18_row115_col2\" class=\"data row115 col2\" >Generates a detailed analysis for the provided time series dataset....</td>\n",
-              "      <td id=\"T_91c18_row115_col3\" class=\"data row115 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row115_col4\" class=\"data row115 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row116_col0\" class=\"data row116 col0\" >validmind.data_validation.ANOVAOneWayTable</td>\n",
-              "      <td id=\"T_91c18_row116_col1\" class=\"data row116 col1\" >ANOVA One Way Table</td>\n",
-              "      <td id=\"T_91c18_row116_col2\" class=\"data row116 col2\" >Applies one-way ANOVA (Analysis of Variance) to identify statistically significant numerical features in the...</td>\n",
-              "      <td id=\"T_91c18_row116_col3\" class=\"data row116 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row116_col4\" class=\"data row116 col4\" >{'features': None, 'p_threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row117_col0\" class=\"data row117 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
-              "      <td id=\"T_91c18_row117_col1\" class=\"data row117 col1\" >Target Rate Bar Plots</td>\n",
-              "      <td id=\"T_91c18_row117_col2\" class=\"data row117 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
-              "      <td id=\"T_91c18_row117_col3\" class=\"data row117 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row117_col4\" class=\"data row117 col4\" >{'default_column': None, 'columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row118_col0\" class=\"data row118 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
-              "      <td id=\"T_91c18_row118_col1\" class=\"data row118 col1\" >Pearson Correlation Matrix</td>\n",
-              "      <td id=\"T_91c18_row118_col2\" class=\"data row118 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
-              "      <td id=\"T_91c18_row118_col3\" class=\"data row118 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row118_col4\" class=\"data row118 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row119_col0\" class=\"data row119 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
-              "      <td id=\"T_91c18_row119_col1\" class=\"data row119 col1\" >Feature Target Correlation Plot</td>\n",
-              "      <td id=\"T_91c18_row119_col2\" class=\"data row119 col2\" >Visualizes the correlation between input features and model's target output in a color-coded horizontal bar plot....</td>\n",
-              "      <td id=\"T_91c18_row119_col3\" class=\"data row119 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row119_col4\" class=\"data row119 col4\" >{'features': None, 'fig_height': 600}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row120_col0\" class=\"data row120 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
-              "      <td id=\"T_91c18_row120_col1\" class=\"data row120 col1\" >Tabular Numerical Histograms</td>\n",
-              "      <td id=\"T_91c18_row120_col2\" class=\"data row120 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
-              "      <td id=\"T_91c18_row120_col3\" class=\"data row120 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row120_col4\" class=\"data row120 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row121_col0\" class=\"data row121 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
-              "      <td id=\"T_91c18_row121_col1\" class=\"data row121 col1\" >Isolation Forest Outliers</td>\n",
-              "      <td id=\"T_91c18_row121_col2\" class=\"data row121 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
-              "      <td id=\"T_91c18_row121_col3\" class=\"data row121 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row121_col4\" class=\"data row121 col4\" >{'random_state': 0, 'contamination': 0.1, 'features_columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row122_col0\" class=\"data row122 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
-              "      <td id=\"T_91c18_row122_col1\" class=\"data row122 col1\" >Chi Squared Features Table</td>\n",
-              "      <td id=\"T_91c18_row122_col2\" class=\"data row122 col2\" >Executes Chi-Squared test for each categorical feature against a target column to assess significant association....</td>\n",
-              "      <td id=\"T_91c18_row122_col3\" class=\"data row122 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row122_col4\" class=\"data row122 col4\" >{'cat_features': None, 'p_threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row123_col0\" class=\"data row123 col0\" >validmind.data_validation.HighCardinality</td>\n",
-              "      <td id=\"T_91c18_row123_col1\" class=\"data row123 col1\" >High Cardinality</td>\n",
-              "      <td id=\"T_91c18_row123_col2\" class=\"data row123 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
-              "      <td id=\"T_91c18_row123_col3\" class=\"data row123 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row123_col4\" class=\"data row123 col4\" >{'num_threshold': 100, 'percent_threshold': 0.1, 'threshold_type': 'percent'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row124_col0\" class=\"data row124 col0\" >validmind.data_validation.MissingValues</td>\n",
-              "      <td id=\"T_91c18_row124_col1\" class=\"data row124 col1\" >Missing Values</td>\n",
-              "      <td id=\"T_91c18_row124_col2\" class=\"data row124 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
-              "      <td id=\"T_91c18_row124_col3\" class=\"data row124 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row124_col4\" class=\"data row124 col4\" >{'min_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row125_col0\" class=\"data row125 col0\" >validmind.data_validation.PhillipsPerronArch</td>\n",
-              "      <td id=\"T_91c18_row125_col1\" class=\"data row125 col1\" >Phillips Perron Arch</td>\n",
-              "      <td id=\"T_91c18_row125_col2\" class=\"data row125 col2\" >Executes Phillips-Perron test to assess the stationarity of time series data in each ML model feature....</td>\n",
-              "      <td id=\"T_91c18_row125_col3\" class=\"data row125 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row125_col4\" class=\"data row125 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row126_col0\" class=\"data row126 col0\" >validmind.data_validation.RollingStatsPlot</td>\n",
-              "      <td id=\"T_91c18_row126_col1\" class=\"data row126 col1\" >Rolling Stats Plot</td>\n",
-              "      <td id=\"T_91c18_row126_col2\" class=\"data row126 col2\" >This test evaluates the stationarity of time series data by plotting its rolling mean and standard deviation....</td>\n",
-              "      <td id=\"T_91c18_row126_col3\" class=\"data row126 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row126_col4\" class=\"data row126 col4\" >{'window_size': 12}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row127_col0\" class=\"data row127 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
-              "      <td id=\"T_91c18_row127_col1\" class=\"data row127 col1\" >Tabular Description Tables</td>\n",
-              "      <td id=\"T_91c18_row127_col2\" class=\"data row127 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
-              "      <td id=\"T_91c18_row127_col3\" class=\"data row127 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row127_col4\" class=\"data row127 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row128_col0\" class=\"data row128 col0\" >validmind.data_validation.AutoMA</td>\n",
-              "      <td id=\"T_91c18_row128_col1\" class=\"data row128 col1\" >Auto MA</td>\n",
-              "      <td id=\"T_91c18_row128_col2\" class=\"data row128 col2\" >Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...</td>\n",
-              "      <td id=\"T_91c18_row128_col3\" class=\"data row128 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row128_col4\" class=\"data row128 col4\" >{'max_ma_order': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row129_col0\" class=\"data row129 col0\" >validmind.data_validation.UniqueRows</td>\n",
-              "      <td id=\"T_91c18_row129_col1\" class=\"data row129 col1\" >Unique Rows</td>\n",
-              "      <td id=\"T_91c18_row129_col2\" class=\"data row129 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
-              "      <td id=\"T_91c18_row129_col3\" class=\"data row129 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row129_col4\" class=\"data row129 col4\" >{'min_percent_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row130_col0\" class=\"data row130 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
-              "      <td id=\"T_91c18_row130_col1\" class=\"data row130 col1\" >Too Many Zero Values</td>\n",
-              "      <td id=\"T_91c18_row130_col2\" class=\"data row130 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
-              "      <td id=\"T_91c18_row130_col3\" class=\"data row130 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row130_col4\" class=\"data row130 col4\" >{'max_percent_threshold': 0.03}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row131_col0\" class=\"data row131 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
-              "      <td id=\"T_91c18_row131_col1\" class=\"data row131 col1\" >High Pearson Correlation</td>\n",
-              "      <td id=\"T_91c18_row131_col2\" class=\"data row131 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
-              "      <td id=\"T_91c18_row131_col3\" class=\"data row131 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row131_col4\" class=\"data row131 col4\" >{'max_threshold': 0.3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row132_col0\" class=\"data row132 col0\" >validmind.data_validation.ACFandPACFPlot</td>\n",
-              "      <td id=\"T_91c18_row132_col1\" class=\"data row132 col1\" >AC Fand PACF Plot</td>\n",
-              "      <td id=\"T_91c18_row132_col2\" class=\"data row132 col2\" >Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...</td>\n",
-              "      <td id=\"T_91c18_row132_col3\" class=\"data row132 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row132_col4\" class=\"data row132 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row133_col0\" class=\"data row133 col0\" >validmind.data_validation.BivariateHistograms</td>\n",
-              "      <td id=\"T_91c18_row133_col1\" class=\"data row133 col1\" >Bivariate Histograms</td>\n",
-              "      <td id=\"T_91c18_row133_col2\" class=\"data row133 col2\" >Generates bivariate histograms for paired features, aiding in visual inspection of categorical variables'...</td>\n",
-              "      <td id=\"T_91c18_row133_col3\" class=\"data row133 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row133_col4\" class=\"data row133 col4\" >{'features_pairs': None, 'target_filter': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row134_col0\" class=\"data row134 col0\" >validmind.data_validation.WOEBinTable</td>\n",
-              "      <td id=\"T_91c18_row134_col1\" class=\"data row134 col1\" >WOE Bin Table</td>\n",
-              "      <td id=\"T_91c18_row134_col2\" class=\"data row134 col2\" >Calculates and assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature in a ML model....</td>\n",
-              "      <td id=\"T_91c18_row134_col3\" class=\"data row134 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row134_col4\" class=\"data row134 col4\" >{'breaks_adj': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row135_col0\" class=\"data row135 col0\" >validmind.data_validation.HeatmapFeatureCorrelations</td>\n",
-              "      <td id=\"T_91c18_row135_col1\" class=\"data row135 col1\" >Heatmap Feature Correlations</td>\n",
-              "      <td id=\"T_91c18_row135_col2\" class=\"data row135 col2\" >Creates a heatmap to visually represent correlation patterns between pairs of numerical features in a dataset....</td>\n",
-              "      <td id=\"T_91c18_row135_col3\" class=\"data row135 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row135_col4\" class=\"data row135 col4\" >{'declutter': None, 'fontsize': None, 'num_features': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row136_col0\" class=\"data row136 col0\" >validmind.data_validation.TimeSeriesFrequency</td>\n",
-              "      <td id=\"T_91c18_row136_col1\" class=\"data row136 col1\" >Time Series Frequency</td>\n",
-              "      <td id=\"T_91c18_row136_col2\" class=\"data row136 col2\" >Evaluates consistency of time series data frequency and generates a frequency plot....</td>\n",
-              "      <td id=\"T_91c18_row136_col3\" class=\"data row136 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row136_col4\" class=\"data row136 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row137_col0\" class=\"data row137 col0\" >validmind.data_validation.DatasetSplit</td>\n",
-              "      <td id=\"T_91c18_row137_col1\" class=\"data row137 col1\" >Dataset Split</td>\n",
-              "      <td id=\"T_91c18_row137_col2\" class=\"data row137 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
-              "      <td id=\"T_91c18_row137_col3\" class=\"data row137 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_91c18_row137_col4\" class=\"data row137 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row138_col0\" class=\"data row138 col0\" >validmind.data_validation.SpreadPlot</td>\n",
-              "      <td id=\"T_91c18_row138_col1\" class=\"data row138 col1\" >Spread Plot</td>\n",
-              "      <td id=\"T_91c18_row138_col2\" class=\"data row138 col2\" >Visualizes the spread relationship between pairs of time-series variables in a dataset, thereby aiding in...</td>\n",
-              "      <td id=\"T_91c18_row138_col3\" class=\"data row138 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row138_col4\" class=\"data row138 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row139_col0\" class=\"data row139 col0\" >validmind.data_validation.TimeSeriesLinePlot</td>\n",
-              "      <td id=\"T_91c18_row139_col1\" class=\"data row139 col1\" >Time Series Line Plot</td>\n",
-              "      <td id=\"T_91c18_row139_col2\" class=\"data row139 col2\" >Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....</td>\n",
-              "      <td id=\"T_91c18_row139_col3\" class=\"data row139 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row139_col4\" class=\"data row139 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row140_col0\" class=\"data row140 col0\" >validmind.data_validation.KPSS</td>\n",
-              "      <td id=\"T_91c18_row140_col1\" class=\"data row140 col1\" >KPSS</td>\n",
-              "      <td id=\"T_91c18_row140_col2\" class=\"data row140 col2\" >Executes KPSS unit root test to validate stationarity of time-series data in machine learning model....</td>\n",
-              "      <td id=\"T_91c18_row140_col3\" class=\"data row140 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row140_col4\" class=\"data row140 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row141_col0\" class=\"data row141 col0\" >validmind.data_validation.AutoSeasonality</td>\n",
-              "      <td id=\"T_91c18_row141_col1\" class=\"data row141 col1\" >Auto Seasonality</td>\n",
-              "      <td id=\"T_91c18_row141_col2\" class=\"data row141 col2\" >Automatically identifies and quantifies optimal seasonality in time series data to improve forecasting model...</td>\n",
-              "      <td id=\"T_91c18_row141_col3\" class=\"data row141 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row141_col4\" class=\"data row141 col4\" >{'min_period': 1, 'max_period': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row142_col0\" class=\"data row142 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
-              "      <td id=\"T_91c18_row142_col1\" class=\"data row142 col1\" >Bivariate Scatter Plots</td>\n",
-              "      <td id=\"T_91c18_row142_col2\" class=\"data row142 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of predictor variables in machine...</td>\n",
-              "      <td id=\"T_91c18_row142_col3\" class=\"data row142 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row142_col4\" class=\"data row142 col4\" >{'selected_columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row143_col0\" class=\"data row143 col0\" >validmind.data_validation.EngleGrangerCoint</td>\n",
-              "      <td id=\"T_91c18_row143_col1\" class=\"data row143 col1\" >Engle Granger Coint</td>\n",
-              "      <td id=\"T_91c18_row143_col2\" class=\"data row143 col2\" >Validates co-integration in pairs of time series data using the Engle-Granger test and classifies them as...</td>\n",
-              "      <td id=\"T_91c18_row143_col3\" class=\"data row143 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row143_col4\" class=\"data row143 col4\" >{'threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row144_col0\" class=\"data row144 col0\" >validmind.data_validation.TimeSeriesMissingValues</td>\n",
-              "      <td id=\"T_91c18_row144_col1\" class=\"data row144 col1\" >Time Series Missing Values</td>\n",
-              "      <td id=\"T_91c18_row144_col2\" class=\"data row144 col2\" >Validates time-series data quality by confirming the count of missing values is below a certain threshold....</td>\n",
-              "      <td id=\"T_91c18_row144_col3\" class=\"data row144 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row144_col4\" class=\"data row144 col4\" >{'min_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row145_col0\" class=\"data row145 col0\" >validmind.data_validation.TimeSeriesHistogram</td>\n",
-              "      <td id=\"T_91c18_row145_col1\" class=\"data row145 col1\" >Time Series Histogram</td>\n",
-              "      <td id=\"T_91c18_row145_col2\" class=\"data row145 col2\" >Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....</td>\n",
-              "      <td id=\"T_91c18_row145_col3\" class=\"data row145 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row145_col4\" class=\"data row145 col4\" >{'nbins': 30}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row146_col0\" class=\"data row146 col0\" >validmind.data_validation.LaggedCorrelationHeatmap</td>\n",
-              "      <td id=\"T_91c18_row146_col1\" class=\"data row146 col1\" >Lagged Correlation Heatmap</td>\n",
-              "      <td id=\"T_91c18_row146_col2\" class=\"data row146 col2\" >Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...</td>\n",
-              "      <td id=\"T_91c18_row146_col3\" class=\"data row146 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row146_col4\" class=\"data row146 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row147_col0\" class=\"data row147 col0\" >validmind.data_validation.SeasonalDecompose</td>\n",
-              "      <td id=\"T_91c18_row147_col1\" class=\"data row147 col1\" >Seasonal Decompose</td>\n",
-              "      <td id=\"T_91c18_row147_col2\" class=\"data row147 col2\" >Decomposes dataset features into observed, trend, seasonal, and residual components to identify patterns and...</td>\n",
-              "      <td id=\"T_91c18_row147_col3\" class=\"data row147 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row147_col4\" class=\"data row147 col4\" >{'seasonal_model': 'additive'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row148_col0\" class=\"data row148 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
-              "      <td id=\"T_91c18_row148_col1\" class=\"data row148 col1\" >WOE Bin Plots</td>\n",
-              "      <td id=\"T_91c18_row148_col2\" class=\"data row148 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
-              "      <td id=\"T_91c18_row148_col3\" class=\"data row148 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row148_col4\" class=\"data row148 col4\" >{'breaks_adj': None, 'fig_height': 600, 'fig_width': 500}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row149_col0\" class=\"data row149 col0\" >validmind.data_validation.ClassImbalance</td>\n",
-              "      <td id=\"T_91c18_row149_col1\" class=\"data row149 col1\" >Class Imbalance</td>\n",
-              "      <td id=\"T_91c18_row149_col2\" class=\"data row149 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
-              "      <td id=\"T_91c18_row149_col3\" class=\"data row149 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row149_col4\" class=\"data row149 col4\" >{'min_percent_threshold': 10}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row150_col0\" class=\"data row150 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
-              "      <td id=\"T_91c18_row150_col1\" class=\"data row150 col1\" >IQR Outliers Bar Plot</td>\n",
-              "      <td id=\"T_91c18_row150_col2\" class=\"data row150 col2\" >Visualizes outlier distribution across percentiles in numerical data using Interquartile Range (IQR) method....</td>\n",
-              "      <td id=\"T_91c18_row150_col3\" class=\"data row150 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row150_col4\" class=\"data row150 col4\" >{'threshold': 1.5, 'num_features': None, 'fig_width': 800}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row151_col0\" class=\"data row151 col0\" >validmind.data_validation.DFGLSArch</td>\n",
-              "      <td id=\"T_91c18_row151_col1\" class=\"data row151 col1\" >DFGLS Arch</td>\n",
-              "      <td id=\"T_91c18_row151_col2\" class=\"data row151 col2\" >Executes Dickey-Fuller GLS metric to determine order of integration and check stationarity in time series data....</td>\n",
-              "      <td id=\"T_91c18_row151_col3\" class=\"data row151 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row151_col4\" class=\"data row151 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row152_col0\" class=\"data row152 col0\" >validmind.data_validation.TimeSeriesDescriptiveStatistics</td>\n",
-              "      <td id=\"T_91c18_row152_col1\" class=\"data row152 col1\" >Time Series Descriptive Statistics</td>\n",
-              "      <td id=\"T_91c18_row152_col2\" class=\"data row152 col2\" >Generates a detailed table of descriptive statistics for the provided time series dataset....</td>\n",
-              "      <td id=\"T_91c18_row152_col3\" class=\"data row152 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row152_col4\" class=\"data row152 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row153_col0\" class=\"data row153 col0\" >validmind.data_validation.AutoAR</td>\n",
-              "      <td id=\"T_91c18_row153_col1\" class=\"data row153 col1\" >Auto AR</td>\n",
-              "      <td id=\"T_91c18_row153_col2\" class=\"data row153 col2\" >Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....</td>\n",
-              "      <td id=\"T_91c18_row153_col3\" class=\"data row153 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row153_col4\" class=\"data row153 col4\" >{'max_ar_order': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row154_col0\" class=\"data row154 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
-              "      <td id=\"T_91c18_row154_col1\" class=\"data row154 col1\" >Tabular Date Time Histograms</td>\n",
-              "      <td id=\"T_91c18_row154_col2\" class=\"data row154 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in model's datetime data....</td>\n",
-              "      <td id=\"T_91c18_row154_col3\" class=\"data row154 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row154_col4\" class=\"data row154 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row155_col0\" class=\"data row155 col0\" >validmind.data_validation.ADF</td>\n",
-              "      <td id=\"T_91c18_row155_col1\" class=\"data row155 col1\" >ADF</td>\n",
-              "      <td id=\"T_91c18_row155_col2\" class=\"data row155 col2\" >Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....</td>\n",
-              "      <td id=\"T_91c18_row155_col3\" class=\"data row155 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row155_col4\" class=\"data row155 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row156_col0\" class=\"data row156 col0\" >validmind.data_validation.nlp.Toxicity</td>\n",
-              "      <td id=\"T_91c18_row156_col1\" class=\"data row156 col1\" >Toxicity</td>\n",
-              "      <td id=\"T_91c18_row156_col2\" class=\"data row156 col2\" >Analyzes the toxicity of text data within a dataset using a pre-trained toxicity model....</td>\n",
-              "      <td id=\"T_91c18_row156_col3\" class=\"data row156 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row156_col4\" class=\"data row156 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row157_col0\" class=\"data row157 col0\" >validmind.data_validation.nlp.PolarityAndSubjectivity</td>\n",
-              "      <td id=\"T_91c18_row157_col1\" class=\"data row157 col1\" >Polarity And Subjectivity</td>\n",
-              "      <td id=\"T_91c18_row157_col2\" class=\"data row157 col2\" >Analyzes the polarity and subjectivity of text data within a dataset....</td>\n",
-              "      <td id=\"T_91c18_row157_col3\" class=\"data row157 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row157_col4\" class=\"data row157 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row158_col0\" class=\"data row158 col0\" >validmind.data_validation.nlp.Punctuations</td>\n",
-              "      <td id=\"T_91c18_row158_col1\" class=\"data row158 col1\" >Punctuations</td>\n",
-              "      <td id=\"T_91c18_row158_col2\" class=\"data row158 col2\" >Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....</td>\n",
-              "      <td id=\"T_91c18_row158_col3\" class=\"data row158 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row158_col4\" class=\"data row158 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row159_col0\" class=\"data row159 col0\" >validmind.data_validation.nlp.Sentiment</td>\n",
-              "      <td id=\"T_91c18_row159_col1\" class=\"data row159 col1\" >Sentiment</td>\n",
-              "      <td id=\"T_91c18_row159_col2\" class=\"data row159 col2\" >Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....</td>\n",
-              "      <td id=\"T_91c18_row159_col3\" class=\"data row159 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row159_col4\" class=\"data row159 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row160_col0\" class=\"data row160 col0\" >validmind.data_validation.nlp.CommonWords</td>\n",
-              "      <td id=\"T_91c18_row160_col1\" class=\"data row160 col1\" >Common Words</td>\n",
-              "      <td id=\"T_91c18_row160_col2\" class=\"data row160 col2\" >Identifies and visualizes the 40 most frequent non-stopwords in a specified text column within a dataset....</td>\n",
-              "      <td id=\"T_91c18_row160_col3\" class=\"data row160 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row160_col4\" class=\"data row160 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row161_col0\" class=\"data row161 col0\" >validmind.data_validation.nlp.Hashtags</td>\n",
-              "      <td id=\"T_91c18_row161_col1\" class=\"data row161 col1\" >Hashtags</td>\n",
-              "      <td id=\"T_91c18_row161_col2\" class=\"data row161 col2\" >Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....</td>\n",
-              "      <td id=\"T_91c18_row161_col3\" class=\"data row161 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row161_col4\" class=\"data row161 col4\" >{'top_hashtags': 25}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row162_col0\" class=\"data row162 col0\" >validmind.data_validation.nlp.LanguageDetection</td>\n",
-              "      <td id=\"T_91c18_row162_col1\" class=\"data row162 col1\" >Language Detection</td>\n",
-              "      <td id=\"T_91c18_row162_col2\" class=\"data row162 col2\" >Detects the language of each text entry in a dataset and visualizes the distribution of languages...</td>\n",
-              "      <td id=\"T_91c18_row162_col3\" class=\"data row162 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row162_col4\" class=\"data row162 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row163_col0\" class=\"data row163 col0\" >validmind.data_validation.nlp.Mentions</td>\n",
-              "      <td id=\"T_91c18_row163_col1\" class=\"data row163 col1\" >Mentions</td>\n",
-              "      <td id=\"T_91c18_row163_col2\" class=\"data row163 col2\" >Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....</td>\n",
-              "      <td id=\"T_91c18_row163_col3\" class=\"data row163 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row163_col4\" class=\"data row163 col4\" >{'top_mentions': 25}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row164_col0\" class=\"data row164 col0\" >validmind.data_validation.nlp.TextDescription</td>\n",
-              "      <td id=\"T_91c18_row164_col1\" class=\"data row164 col1\" >Text Description</td>\n",
-              "      <td id=\"T_91c18_row164_col2\" class=\"data row164 col2\" >Performs comprehensive textual analysis on a dataset using NLTK, evaluating various parameters and generating...</td>\n",
-              "      <td id=\"T_91c18_row164_col3\" class=\"data row164 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row164_col4\" class=\"data row164 col4\" >{'unwanted_tokens': {' ', 'dollar', \"''\", 's', 'us', 'ms', \"s'\", '``', 'mr', 'mrs', \"'s\", 'dr'}, 'num_top_words': 3, 'lang': 'english'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_91c18_row165_col0\" class=\"data row165 col0\" >validmind.data_validation.nlp.StopWords</td>\n",
-              "      <td id=\"T_91c18_row165_col1\" class=\"data row165 col1\" >Stop Words</td>\n",
-              "      <td id=\"T_91c18_row165_col2\" class=\"data row165 col2\" >Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....</td>\n",
-              "      <td id=\"T_91c18_row165_col3\" class=\"data row165 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_91c18_row165_col4\" class=\"data row165 col4\" >{'min_percent_threshold': 0.5, 'num_words': 25}</td>\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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x35dc7d790>"
+              "<pandas.io.formats.style.Styler at 0x37556ee20>"
             ]
           },
           "execution_count": 2,
@@ -1317,18 +1912,20 @@
         {
           "data": {
             "text/plain": [
-              "['text_qa',\n",
-              " 'time_series_forecasting',\n",
+              "['time_series_forecasting',\n",
+              " 'feature_extraction',\n",
+              " 'text_qa',\n",
               " 'text_generation',\n",
-              " 'text_summarization',\n",
-              " 'nlp',\n",
-              " 'text_classification',\n",
+              " 'residual_analysis',\n",
               " 'visualization',\n",
-              " 'classification',\n",
-              " 'feature_extraction',\n",
+              " 'text_classification',\n",
               " 'regression',\n",
-              " 'residual_analysis',\n",
-              " 'clustering']"
+              " 'nlp',\n",
+              " 'text_summarization',\n",
+              " 'data_validation',\n",
+              " 'classification',\n",
+              " 'clustering',\n",
+              " 'monitoring']"
             ]
           },
           "execution_count": 3,
@@ -1348,57 +1945,66 @@
         {
           "data": {
             "text/plain": [
-              "['statsmodels',\n",
-              " 'anomaly_detection',\n",
-              " 'text_data',\n",
-              " 'data_quality',\n",
+              "['few_shot',\n",
               " 'ragas',\n",
-              " 'kmeans',\n",
-              " 'stationarity',\n",
-              " 'seasonality',\n",
-              " 'model_metadata',\n",
-              " 'zero_shot',\n",
-              " 'embeddings',\n",
-              " 'tabular_data',\n",
-              " 'qualitative',\n",
-              " 'forecasting',\n",
-              " 'correlation',\n",
-              " 'model_interpretation',\n",
-              " 'model_comparison',\n",
-              " 'feature_importance',\n",
+              " 'bias_and_fairness',\n",
               " 'AUC',\n",
-              " 'analysis',\n",
-              " 'time_series_data',\n",
+              " 'visualization',\n",
               " 'rag_performance',\n",
-              " 'text_embeddings',\n",
+              " 'logistic_regression',\n",
+              " 'model_validation',\n",
+              " 'credit_risk',\n",
+              " 'model_selection',\n",
+              " 'linear_regression',\n",
+              " 'clustering',\n",
+              " 'data_distribution',\n",
               " 'model_explainability',\n",
-              " 'data_validation',\n",
+              " 'frequency_analysis',\n",
+              " 'model_interpretation',\n",
+              " 'time_series_data',\n",
+              " 'forecasting',\n",
+              " 'llm',\n",
               " 'multiclass_classification',\n",
+              " 'data_validation',\n",
               " 'binary_classification',\n",
-              " 'nlp',\n",
-              " 'data_distribution',\n",
-              " 'sklearn',\n",
-              " 'visualization',\n",
-              " 'few_shot',\n",
-              " 'numerical_data',\n",
-              " 'model_predictions',\n",
-              " 'frequency_analysis',\n",
-              " 'model_performance',\n",
+              " 'stationarity',\n",
               " 'senstivity_analysis',\n",
-              " 'logistic_regression',\n",
-              " 'unit_root_test',\n",
-              " 'model_selection',\n",
+              " 'retrieval_performance',\n",
+              " 'categorical_data',\n",
+              " 'seasonality',\n",
+              " 'qualitative',\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",
-              " 'llm',\n",
-              " 'statistical_test',\n",
-              " 'retrieval_performance',\n",
-              " 'model_training',\n",
+              " 'threshold_optimization',\n",
               " 'model_diagnosis',\n",
-              " 'categorical_data',\n",
-              " 'regression',\n",
-              " 'risk_analysis',\n",
-              " 'credit_risk']"
+              " 'feature_selection',\n",
+              " 'data_analysis',\n",
+              " 'statistical_test',\n",
+              " 'embeddings',\n",
+              " 'analysis',\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']"
             ]
           },
           "execution_count": 4,
@@ -1426,74 +2032,82 @@
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             "text/html": [
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-              "<table id=\"T_14afe\">\n",
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               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_14afe_level0_col0\" class=\"col_heading level0 col0\" >Task</th>\n",
-              "      <th id=\"T_14afe_level0_col1\" class=\"col_heading level0 col1\" >Tags</th>\n",
+              "      <th id=\"T_fae72_level0_col0\" class=\"col_heading level0 col0\" >Task</th>\n",
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               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row0_col0\" class=\"data row0 col0\" >text_classification</td>\n",
-              "      <td id=\"T_14afe_row0_col1\" class=\"data row0 col1\" >text_data, ragas, model_metadata, zero_shot, tabular_data, model_comparison, feature_importance, time_series_data, multiclass_classification, binary_classification, nlp, sklearn, visualization, few_shot, frequency_analysis, model_performance, llm, retrieval_performance, model_diagnosis</td>\n",
+              "      <td id=\"T_fae72_row0_col0\" class=\"data row0 col0\" >regression</td>\n",
+              "      <td id=\"T_fae72_row0_col1\" class=\"data row0 col1\" >bias_and_fairness, visualization, model_selection, linear_regression, data_distribution, model_explainability, model_interpretation, time_series_data, forecasting, multiclass_classification, data_validation, binary_classification, stationarity, model_performance, senstivity_analysis, categorical_data, seasonality, data_quality, regression, model_predictions, descriptive_statistics, unit_root_test, metadata, model_diagnosis, feature_selection, data_analysis, statistical_test, analysis, feature_importance, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_training</td>\n",
+              "    </tr>\n",
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+              "      <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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row1_col0\" class=\"data row1 col0\" >text_summarization</td>\n",
-              "      <td id=\"T_14afe_row1_col1\" class=\"data row1 col1\" >time_series_data, rag_performance, dimensionality_reduction, text_data, qualitative, ragas, nlp, llm, model_metadata, visualization, few_shot, retrieval_performance, zero_shot, frequency_analysis, embeddings, tabular_data</td>\n",
+              "      <td id=\"T_fae72_row2_col0\" class=\"data row2 col0\" >text_classification</td>\n",
+              "      <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",
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-              "      <td id=\"T_14afe_row2_col1\" class=\"data row2 col1\" >regression</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row3_col0\" class=\"data row3 col0\" >visualization</td>\n",
-              "      <td id=\"T_14afe_row3_col1\" class=\"data row3 col1\" >regression</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row4_col0\" class=\"data row4 col0\" >regression</td>\n",
-              "      <td id=\"T_14afe_row4_col1\" class=\"data row4 col1\" >statsmodels, text_data, data_quality, stationarity, seasonality, model_metadata, tabular_data, forecasting, correlation, model_interpretation, model_comparison, feature_importance, analysis, time_series_data, model_explainability, data_validation, data_distribution, sklearn, visualization, numerical_data, model_predictions, model_performance, senstivity_analysis, unit_root_test, model_selection, metadata, statistical_test, model_training, categorical_data, risk_analysis</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row5_col0\" class=\"data row5 col0\" >time_series_forecasting</td>\n",
-              "      <td id=\"T_14afe_row5_col1\" class=\"data row5 col1\" >model_explainability, metadata, data_validation, sklearn, visualization, model_training, model_predictions, model_performance</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row6_col0\" class=\"data row6 col0\" >classification</td>\n",
-              "      <td id=\"T_14afe_row6_col1\" class=\"data row6 col1\" >statsmodels, anomaly_detection, text_data, data_quality, model_metadata, tabular_data, correlation, model_comparison, feature_importance, AUC, time_series_data, multiclass_classification, binary_classification, data_distribution, sklearn, visualization, numerical_data, model_performance, logistic_regression, statistical_test, model_diagnosis, categorical_data, risk_analysis, credit_risk</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row7_col0\" class=\"data row7 col0\" >clustering</td>\n",
-              "      <td id=\"T_14afe_row7_col1\" class=\"data row7 col1\" >sklearn, model_performance, kmeans</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row8_col0\" class=\"data row8 col0\" >text_qa</td>\n",
-              "      <td id=\"T_14afe_row8_col1\" class=\"data row8 col1\" >rag_performance, dimensionality_reduction, qualitative, ragas, llm, visualization, retrieval_performance, embeddings</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row9_col0\" class=\"data row9 col0\" >text_generation</td>\n",
-              "      <td id=\"T_14afe_row9_col1\" class=\"data row9 col1\" >rag_performance, dimensionality_reduction, qualitative, ragas, llm, visualization, retrieval_performance, embeddings</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row10_col0\" class=\"data row10 col0\" >feature_extraction</td>\n",
-              "      <td id=\"T_14afe_row10_col1\" class=\"data row10 col1\" >llm, text_embeddings, visualization, text_data</td>\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",
               "    </tr>\n",
               "    <tr>\n",
-              "      <td id=\"T_14afe_row11_col0\" class=\"data row11 col0\" >nlp</td>\n",
-              "      <td id=\"T_14afe_row11_col1\" class=\"data row11 col1\" >data_validation, nlp, text_data</td>\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",
+              "    </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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x36008c910>"
+              "<pandas.io.formats.style.Styler at 0x3618a93a0>"
             ]
           },
           "execution_count": 5,
@@ -1532,274 +2146,418 @@
           "data": {
             "text/html": [
               "<style type=\"text/css\">\n",
-              "#T_3ea84 th {\n",
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-              "#T_3ea84_row0_col0, #T_3ea84_row0_col1, #T_3ea84_row0_col2, #T_3ea84_row0_col3, #T_3ea84_row0_col4, #T_3ea84_row1_col0, #T_3ea84_row1_col1, #T_3ea84_row1_col2, #T_3ea84_row1_col3, #T_3ea84_row1_col4, #T_3ea84_row2_col0, #T_3ea84_row2_col1, #T_3ea84_row2_col2, #T_3ea84_row2_col3, #T_3ea84_row2_col4, #T_3ea84_row3_col0, #T_3ea84_row3_col1, #T_3ea84_row3_col2, #T_3ea84_row3_col3, #T_3ea84_row3_col4, #T_3ea84_row4_col0, #T_3ea84_row4_col1, #T_3ea84_row4_col2, #T_3ea84_row4_col3, #T_3ea84_row4_col4, #T_3ea84_row5_col0, #T_3ea84_row5_col1, #T_3ea84_row5_col2, #T_3ea84_row5_col3, #T_3ea84_row5_col4, #T_3ea84_row6_col0, #T_3ea84_row6_col1, #T_3ea84_row6_col2, #T_3ea84_row6_col3, #T_3ea84_row6_col4, #T_3ea84_row7_col0, #T_3ea84_row7_col1, #T_3ea84_row7_col2, #T_3ea84_row7_col3, #T_3ea84_row7_col4, #T_3ea84_row8_col0, #T_3ea84_row8_col1, #T_3ea84_row8_col2, #T_3ea84_row8_col3, #T_3ea84_row8_col4, #T_3ea84_row9_col0, #T_3ea84_row9_col1, #T_3ea84_row9_col2, #T_3ea84_row9_col3, #T_3ea84_row9_col4, #T_3ea84_row10_col0, #T_3ea84_row10_col1, #T_3ea84_row10_col2, #T_3ea84_row10_col3, #T_3ea84_row10_col4, #T_3ea84_row11_col0, #T_3ea84_row11_col1, #T_3ea84_row11_col2, #T_3ea84_row11_col3, #T_3ea84_row11_col4, #T_3ea84_row12_col0, #T_3ea84_row12_col1, #T_3ea84_row12_col2, #T_3ea84_row12_col3, #T_3ea84_row12_col4, #T_3ea84_row13_col0, #T_3ea84_row13_col1, #T_3ea84_row13_col2, #T_3ea84_row13_col3, #T_3ea84_row13_col4, #T_3ea84_row14_col0, #T_3ea84_row14_col1, #T_3ea84_row14_col2, #T_3ea84_row14_col3, #T_3ea84_row14_col4, #T_3ea84_row15_col0, #T_3ea84_row15_col1, #T_3ea84_row15_col2, #T_3ea84_row15_col3, #T_3ea84_row15_col4, #T_3ea84_row16_col0, #T_3ea84_row16_col1, #T_3ea84_row16_col2, #T_3ea84_row16_col3, #T_3ea84_row16_col4, #T_3ea84_row17_col0, #T_3ea84_row17_col1, #T_3ea84_row17_col2, #T_3ea84_row17_col3, #T_3ea84_row17_col4, #T_3ea84_row18_col0, #T_3ea84_row18_col1, #T_3ea84_row18_col2, #T_3ea84_row18_col3, #T_3ea84_row18_col4, #T_3ea84_row19_col0, #T_3ea84_row19_col1, #T_3ea84_row19_col2, #T_3ea84_row19_col3, #T_3ea84_row19_col4, #T_3ea84_row20_col0, #T_3ea84_row20_col1, #T_3ea84_row20_col2, #T_3ea84_row20_col3, #T_3ea84_row20_col4, #T_3ea84_row21_col0, #T_3ea84_row21_col1, #T_3ea84_row21_col2, #T_3ea84_row21_col3, #T_3ea84_row21_col4, #T_3ea84_row22_col0, #T_3ea84_row22_col1, #T_3ea84_row22_col2, #T_3ea84_row22_col3, #T_3ea84_row22_col4, #T_3ea84_row23_col0, #T_3ea84_row23_col1, #T_3ea84_row23_col2, #T_3ea84_row23_col3, #T_3ea84_row23_col4, #T_3ea84_row24_col0, #T_3ea84_row24_col1, #T_3ea84_row24_col2, #T_3ea84_row24_col3, #T_3ea84_row24_col4, #T_3ea84_row25_col0, #T_3ea84_row25_col1, #T_3ea84_row25_col2, #T_3ea84_row25_col3, #T_3ea84_row25_col4, #T_3ea84_row26_col0, #T_3ea84_row26_col1, #T_3ea84_row26_col2, #T_3ea84_row26_col3, #T_3ea84_row26_col4, #T_3ea84_row27_col0, #T_3ea84_row27_col1, #T_3ea84_row27_col2, #T_3ea84_row27_col3, #T_3ea84_row27_col4, #T_3ea84_row28_col0, #T_3ea84_row28_col1, #T_3ea84_row28_col2, #T_3ea84_row28_col3, #T_3ea84_row28_col4, #T_3ea84_row29_col0, #T_3ea84_row29_col1, #T_3ea84_row29_col2, #T_3ea84_row29_col3, #T_3ea84_row29_col4, #T_3ea84_row30_col0, #T_3ea84_row30_col1, #T_3ea84_row30_col2, #T_3ea84_row30_col3, #T_3ea84_row30_col4, #T_3ea84_row31_col0, #T_3ea84_row31_col1, #T_3ea84_row31_col2, #T_3ea84_row31_col3, #T_3ea84_row31_col4, #T_3ea84_row32_col0, #T_3ea84_row32_col1, #T_3ea84_row32_col2, #T_3ea84_row32_col3, #T_3ea84_row32_col4, #T_3ea84_row33_col0, #T_3ea84_row33_col1, #T_3ea84_row33_col2, #T_3ea84_row33_col3, #T_3ea84_row33_col4, #T_3ea84_row34_col0, #T_3ea84_row34_col1, #T_3ea84_row34_col2, #T_3ea84_row34_col3, #T_3ea84_row34_col4 {\n",
+              "#T_05adb_row0_col0, #T_05adb_row0_col1, #T_05adb_row0_col2, #T_05adb_row0_col3, #T_05adb_row0_col4, #T_05adb_row0_col5, #T_05adb_row0_col6, #T_05adb_row1_col0, #T_05adb_row1_col1, #T_05adb_row1_col2, #T_05adb_row1_col3, #T_05adb_row1_col4, #T_05adb_row1_col5, #T_05adb_row1_col6, #T_05adb_row2_col0, #T_05adb_row2_col1, #T_05adb_row2_col2, #T_05adb_row2_col3, #T_05adb_row2_col4, #T_05adb_row2_col5, #T_05adb_row2_col6, #T_05adb_row3_col0, #T_05adb_row3_col1, #T_05adb_row3_col2, #T_05adb_row3_col3, #T_05adb_row3_col4, #T_05adb_row3_col5, #T_05adb_row3_col6, #T_05adb_row4_col0, #T_05adb_row4_col1, #T_05adb_row4_col2, #T_05adb_row4_col3, #T_05adb_row4_col4, #T_05adb_row4_col5, #T_05adb_row4_col6, #T_05adb_row5_col0, #T_05adb_row5_col1, #T_05adb_row5_col2, #T_05adb_row5_col3, #T_05adb_row5_col4, #T_05adb_row5_col5, #T_05adb_row5_col6, #T_05adb_row6_col0, #T_05adb_row6_col1, #T_05adb_row6_col2, #T_05adb_row6_col3, #T_05adb_row6_col4, #T_05adb_row6_col5, #T_05adb_row6_col6, 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#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",
               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_3ea84\">\n",
+              "<table id=\"T_05adb\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_3ea84_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_3ea84_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_3ea84_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_3ea84_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_3ea84_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\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",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_3ea84_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.ClusterSizeDistribution</td>\n",
-              "      <td id=\"T_3ea84_row0_col1\" class=\"data row0 col1\" >Cluster Size Distribution</td>\n",
-              "      <td id=\"T_3ea84_row0_col2\" class=\"data row0 col2\" >Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing...</td>\n",
-              "      <td id=\"T_3ea84_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row0_col4\" class=\"data row0 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.TimeSeriesR2SquareBySegments</td>\n",
-              "      <td id=\"T_3ea84_row1_col1\" class=\"data row1 col1\" >Time Series R2 Square By Segments</td>\n",
-              "      <td id=\"T_3ea84_row1_col2\" class=\"data row1 col2\" >Plot R-Squared values for each model over specified time segments and generate a bar chart...</td>\n",
-              "      <td id=\"T_3ea84_row1_col3\" class=\"data row1 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row1_col4\" class=\"data row1 col4\" >{'segments': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.RegressionModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_3ea84_row2_col1\" class=\"data row2 col1\" >Regression Models Performance Comparison</td>\n",
-              "      <td id=\"T_3ea84_row2_col2\" class=\"data row2 col2\" >Compares and evaluates the performance of multiple regression models using five different metrics: MAE, MSE, RMSE,...</td>\n",
-              "      <td id=\"T_3ea84_row2_col3\" class=\"data row2 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row2_col4\" class=\"data row2 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.AdjustedMutualInformation</td>\n",
-              "      <td id=\"T_3ea84_row3_col1\" class=\"data row3 col1\" >Adjusted Mutual Information</td>\n",
-              "      <td id=\"T_3ea84_row3_col2\" class=\"data row3 col2\" >Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...</td>\n",
-              "      <td id=\"T_3ea84_row3_col3\" class=\"data row3 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row3_col4\" class=\"data row3 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.SilhouettePlot</td>\n",
-              "      <td id=\"T_3ea84_row4_col1\" class=\"data row4 col1\" >Silhouette Plot</td>\n",
-              "      <td id=\"T_3ea84_row4_col2\" class=\"data row4 col2\" >Calculates and visualizes Silhouette Score, assessing degree of data point suitability to its cluster in ML models....</td>\n",
-              "      <td id=\"T_3ea84_row4_col3\" class=\"data row4 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row4_col4\" class=\"data row4 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row5_col0\" class=\"data row5 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_3ea84_row5_col1\" class=\"data row5 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_3ea84_row5_col2\" class=\"data row5 col2\" >Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...</td>\n",
-              "      <td id=\"T_3ea84_row5_col3\" class=\"data row5 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row5_col4\" class=\"data row5 col4\" >{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row6_col0\" class=\"data row6 col0\" >validmind.model_validation.sklearn.AdjustedRandIndex</td>\n",
-              "      <td id=\"T_3ea84_row6_col1\" class=\"data row6 col1\" >Adjusted Rand Index</td>\n",
-              "      <td id=\"T_3ea84_row6_col2\" class=\"data row6 col2\" >Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...</td>\n",
-              "      <td id=\"T_3ea84_row6_col3\" class=\"data row6 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row6_col4\" class=\"data row6 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_3ea84_row7_col1\" class=\"data row7 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_3ea84_row7_col2\" class=\"data row7 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_3ea84_row7_col3\" class=\"data row7 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row7_col4\" class=\"data row7 col4\" >{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_3ea84_row8_col1\" class=\"data row8 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_3ea84_row8_col2\" class=\"data row8 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_3ea84_row8_col3\" class=\"data row8 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row8_col4\" class=\"data row8 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.sklearn.HomogeneityScore</td>\n",
-              "      <td id=\"T_3ea84_row9_col1\" class=\"data row9 col1\" >Homogeneity Score</td>\n",
-              "      <td id=\"T_3ea84_row9_col2\" class=\"data row9 col2\" >Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...</td>\n",
-              "      <td id=\"T_3ea84_row9_col3\" class=\"data row9 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row9_col4\" class=\"data row9 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.sklearn.CompletenessScore</td>\n",
-              "      <td id=\"T_3ea84_row10_col1\" class=\"data row10 col1\" >Completeness Score</td>\n",
-              "      <td id=\"T_3ea84_row10_col2\" class=\"data row10 col2\" >Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....</td>\n",
-              "      <td id=\"T_3ea84_row10_col3\" class=\"data row10 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row10_col4\" class=\"data row10 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_3ea84_row11_col1\" class=\"data row11 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_3ea84_row11_col2\" class=\"data row11 col2\" >Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....</td>\n",
-              "      <td id=\"T_3ea84_row11_col3\" class=\"data row11 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row11_col4\" class=\"data row11 col4\" >{'features_columns': None, 'cut_off_percentage': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.sklearn.ClusterPerformanceMetrics</td>\n",
-              "      <td id=\"T_3ea84_row12_col1\" class=\"data row12 col1\" >Cluster Performance Metrics</td>\n",
-              "      <td id=\"T_3ea84_row12_col2\" class=\"data row12 col2\" >Evaluates the performance of clustering machine learning models using multiple established metrics....</td>\n",
-              "      <td id=\"T_3ea84_row12_col3\" class=\"data row12 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row12_col4\" class=\"data row12 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_3ea84_row13_col1\" class=\"data row13 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_3ea84_row13_col2\" class=\"data row13 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_3ea84_row13_col3\" class=\"data row13 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row13_col4\" class=\"data row13 col4\" >{'fontsize': None, 'figure_height': 1000}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.sklearn.FowlkesMallowsScore</td>\n",
-              "      <td id=\"T_3ea84_row14_col1\" class=\"data row14 col1\" >Fowlkes Mallows Score</td>\n",
-              "      <td id=\"T_3ea84_row14_col2\" class=\"data row14 col2\" >Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...</td>\n",
-              "      <td id=\"T_3ea84_row14_col3\" class=\"data row14 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row14_col4\" class=\"data row14 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_3ea84_row15_col1\" class=\"data row15 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_3ea84_row15_col2\" class=\"data row15 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_3ea84_row15_col3\" class=\"data row15 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row15_col4\" class=\"data row15 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.sklearn.ClusterCosineSimilarity</td>\n",
-              "      <td id=\"T_3ea84_row16_col1\" class=\"data row16 col1\" >Cluster Cosine Similarity</td>\n",
-              "      <td id=\"T_3ea84_row16_col2\" class=\"data row16 col2\" >Measures the intra-cluster similarity of a clustering model using cosine similarity....</td>\n",
-              "      <td id=\"T_3ea84_row16_col3\" class=\"data row16 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row16_col4\" class=\"data row16 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_3ea84_row17_col1\" class=\"data row17 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_3ea84_row17_col2\" class=\"data row17 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_3ea84_row17_col3\" class=\"data row17 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row17_col4\" class=\"data row17 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_3ea84_row18_col1\" class=\"data row18 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_3ea84_row18_col2\" class=\"data row18 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_3ea84_row18_col3\" class=\"data row18 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row18_col4\" class=\"data row18 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.sklearn.VMeasure</td>\n",
-              "      <td id=\"T_3ea84_row19_col1\" class=\"data row19 col1\" >V Measure</td>\n",
-              "      <td id=\"T_3ea84_row19_col2\" class=\"data row19 col2\" >Evaluates homogeneity and completeness of a clustering model using the V Measure Score....</td>\n",
-              "      <td id=\"T_3ea84_row19_col3\" class=\"data row19 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row19_col4\" class=\"data row19 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_3ea84_row20_col1\" class=\"data row20 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_3ea84_row20_col2\" class=\"data row20 col2\" >Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....</td>\n",
-              "      <td id=\"T_3ea84_row20_col3\" class=\"data row20 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row20_col4\" class=\"data row20 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_3ea84_row21_col1\" class=\"data row21 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_3ea84_row21_col2\" class=\"data row21 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_3ea84_row21_col3\" class=\"data row21 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row21_col4\" class=\"data row21 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.sklearn.RegressionR2Square</td>\n",
-              "      <td id=\"T_3ea84_row22_col1\" class=\"data row22 col1\" >Regression R2 Square</td>\n",
-              "      <td id=\"T_3ea84_row22_col2\" class=\"data row22 col2\" >**Purpose**: The purpose of the RegressionR2Square Metric test is to measure the overall goodness-of-fit of a...</td>\n",
-              "      <td id=\"T_3ea84_row22_col3\" class=\"data row22 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row22_col4\" class=\"data row22 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.sklearn.RegressionErrors</td>\n",
-              "      <td id=\"T_3ea84_row23_col1\" class=\"data row23 col1\" >Regression Errors</td>\n",
-              "      <td id=\"T_3ea84_row23_col2\" class=\"data row23 col2\" >**Purpose**: This metric is used to measure the performance of a regression model. It gauges the model's accuracy...</td>\n",
-              "      <td id=\"T_3ea84_row23_col3\" class=\"data row23 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row23_col4\" class=\"data row23 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.sklearn.ClusterPerformance</td>\n",
-              "      <td id=\"T_3ea84_row24_col1\" class=\"data row24 col1\" >Cluster Performance</td>\n",
-              "      <td id=\"T_3ea84_row24_col2\" class=\"data row24 col2\" >Evaluates and compares a clustering model's performance on training and testing datasets using multiple defined...</td>\n",
-              "      <td id=\"T_3ea84_row24_col3\" class=\"data row24 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row24_col4\" class=\"data row24 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.sklearn.FeatureImportanceComparison</td>\n",
-              "      <td id=\"T_3ea84_row25_col1\" class=\"data row25 col1\" >Feature Importance Comparison</td>\n",
-              "      <td id=\"T_3ea84_row25_col2\" class=\"data row25 col2\" >Compare feature importance scores for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_3ea84_row25_col3\" class=\"data row25 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row25_col4\" class=\"data row25 col4\" >{'num_features': 3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_3ea84_row26_col1\" class=\"data row26 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_3ea84_row26_col2\" class=\"data row26 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_3ea84_row26_col3\" class=\"data row26 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row26_col4\" class=\"data row26 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row27_col0\" class=\"data row27 col0\" >validmind.model_validation.sklearn.RegressionErrorsComparison</td>\n",
-              "      <td id=\"T_3ea84_row27_col1\" class=\"data row27 col1\" >Regression Errors Comparison</td>\n",
-              "      <td id=\"T_3ea84_row27_col2\" class=\"data row27 col2\" >Compare regression error metrics for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_3ea84_row27_col3\" class=\"data row27 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row27_col4\" class=\"data row27 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row28_col0\" class=\"data row28 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_3ea84_row28_col1\" class=\"data row28 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_3ea84_row28_col2\" class=\"data row28 col2\" >Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....</td>\n",
-              "      <td id=\"T_3ea84_row28_col3\" class=\"data row28 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row28_col4\" class=\"data row28 col4\" >{'param_grid': None, 'scoring': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row29_col0\" class=\"data row29 col0\" >validmind.model_validation.sklearn.KMeansClustersOptimization</td>\n",
-              "      <td id=\"T_3ea84_row29_col1\" class=\"data row29 col1\" >K Means Clusters Optimization</td>\n",
-              "      <td id=\"T_3ea84_row29_col2\" class=\"data row29 col2\" >Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....</td>\n",
-              "      <td id=\"T_3ea84_row29_col3\" class=\"data row29 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row29_col4\" class=\"data row29 col4\" >{'n_clusters': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row30_col0\" class=\"data row30 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_3ea84_row30_col1\" class=\"data row30 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_3ea84_row30_col2\" class=\"data row30 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_3ea84_row30_col3\" class=\"data row30 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row30_col4\" class=\"data row30 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row31_col0\" class=\"data row31 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_3ea84_row31_col1\" class=\"data row31 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_3ea84_row31_col2\" class=\"data row31 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_3ea84_row31_col3\" class=\"data row31 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row31_col4\" class=\"data row31 col4\" >{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row32_col0\" class=\"data row32 col0\" >validmind.model_validation.sklearn.RegressionR2SquareComparison</td>\n",
-              "      <td id=\"T_3ea84_row32_col1\" class=\"data row32 col1\" >Regression R2 Square Comparison</td>\n",
-              "      <td id=\"T_3ea84_row32_col2\" class=\"data row32 col2\" >Compare R-Squared and Adjusted R-Squared values for each model and generate a summary table...</td>\n",
-              "      <td id=\"T_3ea84_row32_col3\" class=\"data row32 col3\" >['datasets', 'models']</td>\n",
-              "      <td id=\"T_3ea84_row32_col4\" class=\"data row32 col4\" >{}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row33_col0\" class=\"data row33 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_3ea84_row33_col1\" class=\"data row33 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_3ea84_row33_col2\" class=\"data row33 col2\" >Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_3ea84_row33_col3\" class=\"data row33 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_3ea84_row33_col4\" class=\"data row33 col4\" >{'num_bins': 10, 'mode': 'fixed'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_3ea84_row34_col0\" class=\"data row34 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_3ea84_row34_col1\" class=\"data row34 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_3ea84_row34_col2\" class=\"data row34 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_3ea84_row34_col3\" class=\"data row34 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_3ea84_row34_col4\" class=\"data row34 col4\" >{'min_threshold': 0.7}</td>\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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x35dc84290>"
+              "<pandas.io.formats.style.Styler at 0x3618a9580>"
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           },
           "execution_count": 6,
@@ -1827,442 +2585,715 @@
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#T_fe4d0_row47_col2, #T_fe4d0_row47_col3, #T_fe4d0_row47_col4, #T_fe4d0_row48_col0, #T_fe4d0_row48_col1, #T_fe4d0_row48_col2, #T_fe4d0_row48_col3, #T_fe4d0_row48_col4, #T_fe4d0_row49_col0, #T_fe4d0_row49_col1, #T_fe4d0_row49_col2, #T_fe4d0_row49_col3, #T_fe4d0_row49_col4, #T_fe4d0_row50_col0, #T_fe4d0_row50_col1, #T_fe4d0_row50_col2, #T_fe4d0_row50_col3, #T_fe4d0_row50_col4, #T_fe4d0_row51_col0, #T_fe4d0_row51_col1, #T_fe4d0_row51_col2, #T_fe4d0_row51_col3, #T_fe4d0_row51_col4, #T_fe4d0_row52_col0, #T_fe4d0_row52_col1, #T_fe4d0_row52_col2, #T_fe4d0_row52_col3, #T_fe4d0_row52_col4, #T_fe4d0_row53_col0, #T_fe4d0_row53_col1, #T_fe4d0_row53_col2, #T_fe4d0_row53_col3, #T_fe4d0_row53_col4, #T_fe4d0_row54_col0, #T_fe4d0_row54_col1, #T_fe4d0_row54_col2, #T_fe4d0_row54_col3, #T_fe4d0_row54_col4, #T_fe4d0_row55_col0, #T_fe4d0_row55_col1, #T_fe4d0_row55_col2, #T_fe4d0_row55_col3, #T_fe4d0_row55_col4, #T_fe4d0_row56_col0, #T_fe4d0_row56_col1, #T_fe4d0_row56_col2, #T_fe4d0_row56_col3, #T_fe4d0_row56_col4, #T_fe4d0_row57_col0, #T_fe4d0_row57_col1, #T_fe4d0_row57_col2, #T_fe4d0_row57_col3, #T_fe4d0_row57_col4, #T_fe4d0_row58_col0, #T_fe4d0_row58_col1, #T_fe4d0_row58_col2, #T_fe4d0_row58_col3, #T_fe4d0_row58_col4 {\n",
+              "#T_686e6_row0_col0, #T_686e6_row0_col1, #T_686e6_row0_col2, #T_686e6_row0_col3, #T_686e6_row0_col4, #T_686e6_row0_col5, #T_686e6_row0_col6, #T_686e6_row1_col0, #T_686e6_row1_col1, #T_686e6_row1_col2, #T_686e6_row1_col3, #T_686e6_row1_col4, #T_686e6_row1_col5, #T_686e6_row1_col6, #T_686e6_row2_col0, #T_686e6_row2_col1, #T_686e6_row2_col2, #T_686e6_row2_col3, #T_686e6_row2_col4, #T_686e6_row2_col5, #T_686e6_row2_col6, #T_686e6_row3_col0, #T_686e6_row3_col1, #T_686e6_row3_col2, #T_686e6_row3_col3, #T_686e6_row3_col4, #T_686e6_row3_col5, #T_686e6_row3_col6, #T_686e6_row4_col0, #T_686e6_row4_col1, #T_686e6_row4_col2, #T_686e6_row4_col3, #T_686e6_row4_col4, #T_686e6_row4_col5, #T_686e6_row4_col6, #T_686e6_row5_col0, #T_686e6_row5_col1, #T_686e6_row5_col2, #T_686e6_row5_col3, #T_686e6_row5_col4, #T_686e6_row5_col5, #T_686e6_row5_col6, #T_686e6_row6_col0, #T_686e6_row6_col1, #T_686e6_row6_col2, #T_686e6_row6_col3, #T_686e6_row6_col4, #T_686e6_row6_col5, #T_686e6_row6_col6, 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               "  text-align: left;\n",
               "}\n",
               "</style>\n",
-              "<table id=\"T_fe4d0\">\n",
+              "<table id=\"T_686e6\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_fe4d0_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_fe4d0_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_fe4d0_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_fe4d0_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_fe4d0_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\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",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_fe4d0_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.FeaturesAUC</td>\n",
-              "      <td id=\"T_fe4d0_row0_col1\" class=\"data row0 col1\" >Features AUC</td>\n",
-              "      <td id=\"T_fe4d0_row0_col2\" class=\"data row0 col2\" >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....</td>\n",
-              "      <td id=\"T_fe4d0_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row0_col4\" class=\"data row0 col4\" >{'fontsize': 12, 'figure_height': 500}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.ModelMetadata</td>\n",
-              "      <td id=\"T_fe4d0_row1_col1\" class=\"data row1 col1\" >Model Metadata</td>\n",
-              "      <td id=\"T_fe4d0_row1_col2\" class=\"data row1 col2\" >Extracts and summarizes critical metadata from a machine learning model instance for comprehensive analysis....</td>\n",
-              "      <td id=\"T_fe4d0_row1_col3\" class=\"data row1 col3\" >['model']</td>\n",
-              "      <td id=\"T_fe4d0_row1_col4\" class=\"data row1 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.RobustnessDiagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row2_col1\" class=\"data row2 col1\" >Robustness Diagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row2_col2\" class=\"data row2 col2\" >Evaluates the robustness of a machine learning model by injecting Gaussian noise to input data and measuring...</td>\n",
-              "      <td id=\"T_fe4d0_row2_col3\" class=\"data row2 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row2_col4\" class=\"data row2 col4\" >{'features_columns': None, 'scaling_factor_std_dev_list': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], 'accuracy_decay_threshold': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.SHAPGlobalImportance</td>\n",
-              "      <td id=\"T_fe4d0_row3_col1\" class=\"data row3 col1\" >SHAP Global Importance</td>\n",
-              "      <td id=\"T_fe4d0_row3_col2\" class=\"data row3 col2\" >Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....</td>\n",
-              "      <td id=\"T_fe4d0_row3_col3\" class=\"data row3 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row3_col4\" class=\"data row3 col4\" >{'kernel_explainer_samples': 10, 'tree_or_linear_explainer_samples': 200}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_fe4d0_row4_col1\" class=\"data row4 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_fe4d0_row4_col2\" class=\"data row4 col2\" >Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...</td>\n",
-              "      <td id=\"T_fe4d0_row4_col3\" class=\"data row4 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row4_col4\" class=\"data row4 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row5_col0\" class=\"data row5 col0\" >validmind.model_validation.sklearn.OverfitDiagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row5_col1\" class=\"data row5 col1\" >Overfit Diagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row5_col2\" class=\"data row5 col2\" >Detects and visualizes overfit regions in an ML model by comparing performance on training and test datasets....</td>\n",
-              "      <td id=\"T_fe4d0_row5_col3\" class=\"data row5 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row5_col4\" class=\"data row5 col4\" >{'features_columns': None, 'cut_off_percentage': 4}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row6_col0\" class=\"data row6 col0\" >validmind.model_validation.sklearn.PermutationFeatureImportance</td>\n",
-              "      <td id=\"T_fe4d0_row6_col1\" class=\"data row6 col1\" >Permutation Feature Importance</td>\n",
-              "      <td id=\"T_fe4d0_row6_col2\" class=\"data row6 col2\" >Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...</td>\n",
-              "      <td id=\"T_fe4d0_row6_col3\" class=\"data row6 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row6_col4\" class=\"data row6 col4\" >{'fontsize': None, 'figure_height': 1000}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row7_col0\" class=\"data row7 col0\" >validmind.model_validation.sklearn.MinimumROCAUCScore</td>\n",
-              "      <td id=\"T_fe4d0_row7_col1\" class=\"data row7 col1\" >Minimum ROCAUC Score</td>\n",
-              "      <td id=\"T_fe4d0_row7_col2\" class=\"data row7 col2\" >Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row7_col3\" class=\"data row7 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row7_col4\" class=\"data row7 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row8_col0\" class=\"data row8 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_fe4d0_row8_col1\" class=\"data row8 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_fe4d0_row8_col2\" class=\"data row8 col2\" >Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....</td>\n",
-              "      <td id=\"T_fe4d0_row8_col3\" class=\"data row8 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row8_col4\" class=\"data row8 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row9_col0\" class=\"data row9 col0\" >validmind.model_validation.sklearn.ClassifierPerformance</td>\n",
-              "      <td id=\"T_fe4d0_row9_col1\" class=\"data row9 col1\" >Classifier Performance</td>\n",
-              "      <td id=\"T_fe4d0_row9_col2\" class=\"data row9 col2\" >Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...</td>\n",
-              "      <td id=\"T_fe4d0_row9_col3\" class=\"data row9 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row9_col4\" class=\"data row9 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row10_col0\" class=\"data row10 col0\" >validmind.model_validation.sklearn.MinimumF1Score</td>\n",
-              "      <td id=\"T_fe4d0_row10_col1\" class=\"data row10 col1\" >Minimum F1 Score</td>\n",
-              "      <td id=\"T_fe4d0_row10_col2\" class=\"data row10 col2\" >Evaluates if the model's F1 score on the validation set meets a predefined minimum threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row10_col3\" class=\"data row10 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row10_col4\" class=\"data row10 col4\" >{'min_threshold': 0.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row11_col0\" class=\"data row11 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_fe4d0_row11_col1\" class=\"data row11 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_fe4d0_row11_col2\" class=\"data row11 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_fe4d0_row11_col3\" class=\"data row11 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row11_col4\" class=\"data row11 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row12_col0\" class=\"data row12 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_fe4d0_row12_col1\" class=\"data row12 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_fe4d0_row12_col2\" class=\"data row12 col2\" >Tests if model performance degradation between training and test datasets exceeds a predefined threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row12_col3\" class=\"data row12 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row12_col4\" class=\"data row12 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row13_col0\" class=\"data row13 col0\" >validmind.model_validation.sklearn.HyperParametersTuning</td>\n",
-              "      <td id=\"T_fe4d0_row13_col1\" class=\"data row13 col1\" >Hyper Parameters Tuning</td>\n",
-              "      <td id=\"T_fe4d0_row13_col2\" class=\"data row13 col2\" >Exerts exhaustive grid search to identify optimal hyperparameters for the model, improving performance....</td>\n",
-              "      <td id=\"T_fe4d0_row13_col3\" class=\"data row13 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row13_col4\" class=\"data row13 col4\" >{'param_grid': None, 'scoring': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row14_col0\" class=\"data row14 col0\" >validmind.model_validation.sklearn.ModelsPerformanceComparison</td>\n",
-              "      <td id=\"T_fe4d0_row14_col1\" class=\"data row14 col1\" >Models Performance Comparison</td>\n",
-              "      <td id=\"T_fe4d0_row14_col2\" class=\"data row14 col2\" >Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...</td>\n",
-              "      <td id=\"T_fe4d0_row14_col3\" class=\"data row14 col3\" >['dataset', 'models']</td>\n",
-              "      <td id=\"T_fe4d0_row14_col4\" class=\"data row14 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row15_col0\" class=\"data row15 col0\" >validmind.model_validation.sklearn.WeakspotsDiagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row15_col1\" class=\"data row15 col1\" >Weakspots Diagnosis</td>\n",
-              "      <td id=\"T_fe4d0_row15_col2\" class=\"data row15 col2\" >Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...</td>\n",
-              "      <td id=\"T_fe4d0_row15_col3\" class=\"data row15 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row15_col4\" class=\"data row15 col4\" >{'features_columns': None, 'thresholds': {'accuracy': 0.75, 'precision': 0.5, 'recall': 0.5, 'f1': 0.7}}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row16_col0\" class=\"data row16 col0\" >validmind.model_validation.sklearn.PopulationStabilityIndex</td>\n",
-              "      <td id=\"T_fe4d0_row16_col1\" class=\"data row16 col1\" >Population Stability Index</td>\n",
-              "      <td id=\"T_fe4d0_row16_col2\" class=\"data row16 col2\" >Evaluates the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...</td>\n",
-              "      <td id=\"T_fe4d0_row16_col3\" class=\"data row16 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row16_col4\" class=\"data row16 col4\" >{'num_bins': 10, 'mode': 'fixed'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row17_col0\" class=\"data row17 col0\" >validmind.model_validation.sklearn.MinimumAccuracy</td>\n",
-              "      <td id=\"T_fe4d0_row17_col1\" class=\"data row17 col1\" >Minimum Accuracy</td>\n",
-              "      <td id=\"T_fe4d0_row17_col2\" class=\"data row17 col2\" >Checks if the model's prediction accuracy meets or surpasses a specified threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row17_col3\" class=\"data row17 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row17_col4\" class=\"data row17 col4\" >{'min_threshold': 0.7}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row18_col0\" class=\"data row18 col0\" >validmind.model_validation.statsmodels.ScorecardHistogram</td>\n",
-              "      <td id=\"T_fe4d0_row18_col1\" class=\"data row18 col1\" >Scorecard Histogram</td>\n",
-              "      <td id=\"T_fe4d0_row18_col2\" class=\"data row18 col2\" >Creates histograms of credit scores, from both default and non-default instances, generated by a credit-risk model....</td>\n",
-              "      <td id=\"T_fe4d0_row18_col3\" class=\"data row18 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row18_col4\" class=\"data row18 col4\" >{'title': 'Histogram of Scores', 'score_column': 'score'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row19_col0\" class=\"data row19 col0\" >validmind.model_validation.statsmodels.JarqueBera</td>\n",
-              "      <td id=\"T_fe4d0_row19_col1\" class=\"data row19 col1\" >Jarque Bera</td>\n",
-              "      <td id=\"T_fe4d0_row19_col2\" class=\"data row19 col2\" >Assesses normality of dataset features in an ML model using the Jarque-Bera test....</td>\n",
-              "      <td id=\"T_fe4d0_row19_col3\" class=\"data row19 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row19_col4\" class=\"data row19 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row20_col0\" class=\"data row20 col0\" >validmind.model_validation.statsmodels.KolmogorovSmirnov</td>\n",
-              "      <td id=\"T_fe4d0_row20_col1\" class=\"data row20 col1\" >Kolmogorov Smirnov</td>\n",
-              "      <td id=\"T_fe4d0_row20_col2\" class=\"data row20 col2\" >Executes a feature-wise Kolmogorov-Smirnov test to evaluate alignment with normal distribution in datasets....</td>\n",
-              "      <td id=\"T_fe4d0_row20_col3\" class=\"data row20 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row20_col4\" class=\"data row20 col4\" >{'dist': 'norm'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row21_col0\" class=\"data row21 col0\" >validmind.model_validation.statsmodels.ShapiroWilk</td>\n",
-              "      <td id=\"T_fe4d0_row21_col1\" class=\"data row21 col1\" >Shapiro Wilk</td>\n",
-              "      <td id=\"T_fe4d0_row21_col2\" class=\"data row21 col2\" >Evaluates feature-wise normality of training data using the Shapiro-Wilk test....</td>\n",
-              "      <td id=\"T_fe4d0_row21_col3\" class=\"data row21 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row21_col4\" class=\"data row21 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row22_col0\" class=\"data row22 col0\" >validmind.model_validation.statsmodels.CumulativePredictionProbabilities</td>\n",
-              "      <td id=\"T_fe4d0_row22_col1\" class=\"data row22 col1\" >Cumulative Prediction Probabilities</td>\n",
-              "      <td id=\"T_fe4d0_row22_col2\" class=\"data row22 col2\" >Visualizes cumulative probabilities of positive and negative classes for both training and testing in logistic...</td>\n",
-              "      <td id=\"T_fe4d0_row22_col3\" class=\"data row22 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row22_col4\" class=\"data row22 col4\" >{'title': 'Cumulative Probabilities'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row23_col0\" class=\"data row23 col0\" >validmind.model_validation.statsmodels.Lilliefors</td>\n",
-              "      <td id=\"T_fe4d0_row23_col1\" class=\"data row23 col1\" >Lilliefors</td>\n",
-              "      <td id=\"T_fe4d0_row23_col2\" class=\"data row23 col2\" >Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....</td>\n",
-              "      <td id=\"T_fe4d0_row23_col3\" class=\"data row23 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row23_col4\" class=\"data row23 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row24_col0\" class=\"data row24 col0\" >validmind.model_validation.statsmodels.RunsTest</td>\n",
-              "      <td id=\"T_fe4d0_row24_col1\" class=\"data row24 col1\" >Runs Test</td>\n",
-              "      <td id=\"T_fe4d0_row24_col2\" class=\"data row24 col2\" >Executes Runs Test on ML model to detect non-random patterns in output data sequence....</td>\n",
-              "      <td id=\"T_fe4d0_row24_col3\" class=\"data row24 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row24_col4\" class=\"data row24 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row25_col0\" class=\"data row25 col0\" >validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram</td>\n",
-              "      <td id=\"T_fe4d0_row25_col1\" class=\"data row25 col1\" >Prediction Probabilities Histogram</td>\n",
-              "      <td id=\"T_fe4d0_row25_col2\" class=\"data row25 col2\" >Generates and visualizes histograms of the Probability of Default predictions for both positive and negative...</td>\n",
-              "      <td id=\"T_fe4d0_row25_col3\" class=\"data row25 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row25_col4\" class=\"data row25 col4\" >{'title': 'Histogram of Predictive Probabilities'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row26_col0\" class=\"data row26 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
-              "      <td id=\"T_fe4d0_row26_col1\" class=\"data row26 col1\" >GINI Table</td>\n",
-              "      <td id=\"T_fe4d0_row26_col2\" class=\"data row26 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
-              "      <td id=\"T_fe4d0_row26_col3\" class=\"data row26 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row26_col4\" class=\"data row26 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row27_col0\" class=\"data row27 col0\" >validmind.data_validation.MissingValuesRisk</td>\n",
-              "      <td id=\"T_fe4d0_row27_col1\" class=\"data row27 col1\" >Missing Values Risk</td>\n",
-              "      <td id=\"T_fe4d0_row27_col2\" class=\"data row27 col2\" >Assesses and quantifies the risk related to missing values in a dataset used for training an ML model....</td>\n",
-              "      <td id=\"T_fe4d0_row27_col3\" class=\"data row27 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row27_col4\" class=\"data row27 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row28_col0\" class=\"data row28 col0\" >validmind.data_validation.IQROutliersTable</td>\n",
-              "      <td id=\"T_fe4d0_row28_col1\" class=\"data row28 col1\" >IQR Outliers Table</td>\n",
-              "      <td id=\"T_fe4d0_row28_col2\" class=\"data row28 col2\" >Determines and summarizes outliers in numerical features using Interquartile Range method....</td>\n",
-              "      <td id=\"T_fe4d0_row28_col3\" class=\"data row28 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row28_col4\" class=\"data row28 col4\" >{'features': None, 'threshold': 1.5}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row29_col0\" class=\"data row29 col0\" >validmind.data_validation.BivariateFeaturesBarPlots</td>\n",
-              "      <td id=\"T_fe4d0_row29_col1\" class=\"data row29 col1\" >Bivariate Features Bar Plots</td>\n",
-              "      <td id=\"T_fe4d0_row29_col2\" class=\"data row29 col2\" >Generates visual bar plots to analyze the relationship between paired features within categorical data in the model....</td>\n",
-              "      <td id=\"T_fe4d0_row29_col3\" class=\"data row29 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row29_col4\" class=\"data row29 col4\" >{'features_pairs': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row30_col0\" class=\"data row30 col0\" >validmind.data_validation.Skewness</td>\n",
-              "      <td id=\"T_fe4d0_row30_col1\" class=\"data row30 col1\" >Skewness</td>\n",
-              "      <td id=\"T_fe4d0_row30_col2\" class=\"data row30 col2\" >Evaluates the skewness of numerical data in a machine learning model and checks if it falls below a set maximum...</td>\n",
-              "      <td id=\"T_fe4d0_row30_col3\" class=\"data row30 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row30_col4\" class=\"data row30 col4\" >{'max_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row31_col0\" class=\"data row31 col0\" >validmind.data_validation.Duplicates</td>\n",
-              "      <td id=\"T_fe4d0_row31_col1\" class=\"data row31 col1\" >Duplicates</td>\n",
-              "      <td id=\"T_fe4d0_row31_col2\" class=\"data row31 col2\" >Tests dataset for duplicate entries, ensuring model reliability via data quality verification....</td>\n",
-              "      <td id=\"T_fe4d0_row31_col3\" class=\"data row31 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row31_col4\" class=\"data row31 col4\" >{'min_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row32_col0\" class=\"data row32 col0\" >validmind.data_validation.MissingValuesBarPlot</td>\n",
-              "      <td id=\"T_fe4d0_row32_col1\" class=\"data row32 col1\" >Missing Values Bar Plot</td>\n",
-              "      <td id=\"T_fe4d0_row32_col2\" class=\"data row32 col2\" >Creates a bar plot showcasing the percentage of missing values in each column of the dataset with risk...</td>\n",
-              "      <td id=\"T_fe4d0_row32_col3\" class=\"data row32 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row32_col4\" class=\"data row32 col4\" >{'threshold': 80, 'fig_height': 600}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row33_col0\" class=\"data row33 col0\" >validmind.data_validation.DatasetDescription</td>\n",
-              "      <td id=\"T_fe4d0_row33_col1\" class=\"data row33 col1\" >Dataset Description</td>\n",
-              "      <td id=\"T_fe4d0_row33_col2\" class=\"data row33 col2\" >Provides comprehensive analysis and statistical summaries of each field in a machine learning model's dataset....</td>\n",
-              "      <td id=\"T_fe4d0_row33_col3\" class=\"data row33 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row33_col4\" class=\"data row33 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row34_col0\" class=\"data row34 col0\" >validmind.data_validation.ScatterPlot</td>\n",
-              "      <td id=\"T_fe4d0_row34_col1\" class=\"data row34 col1\" >Scatter Plot</td>\n",
-              "      <td id=\"T_fe4d0_row34_col2\" class=\"data row34 col2\" >Creates a scatter plot matrix to visually analyze feature relationships, patterns, and outliers in a dataset....</td>\n",
-              "      <td id=\"T_fe4d0_row34_col3\" class=\"data row34 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row34_col4\" class=\"data row34 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row35_col0\" class=\"data row35 col0\" >validmind.data_validation.TabularCategoricalBarPlots</td>\n",
-              "      <td id=\"T_fe4d0_row35_col1\" class=\"data row35 col1\" >Tabular Categorical Bar Plots</td>\n",
-              "      <td id=\"T_fe4d0_row35_col2\" class=\"data row35 col2\" >Generates and visualizes bar plots for each category in categorical features to evaluate dataset's composition....</td>\n",
-              "      <td id=\"T_fe4d0_row35_col3\" class=\"data row35 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row35_col4\" class=\"data row35 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row36_col0\" class=\"data row36 col0\" >validmind.data_validation.DescriptiveStatistics</td>\n",
-              "      <td id=\"T_fe4d0_row36_col1\" class=\"data row36 col1\" >Descriptive Statistics</td>\n",
-              "      <td id=\"T_fe4d0_row36_col2\" class=\"data row36 col2\" >Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...</td>\n",
-              "      <td id=\"T_fe4d0_row36_col3\" class=\"data row36 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row36_col4\" class=\"data row36 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row37_col0\" class=\"data row37 col0\" >validmind.data_validation.ANOVAOneWayTable</td>\n",
-              "      <td id=\"T_fe4d0_row37_col1\" class=\"data row37 col1\" >ANOVA One Way Table</td>\n",
-              "      <td id=\"T_fe4d0_row37_col2\" class=\"data row37 col2\" >Applies one-way ANOVA (Analysis of Variance) to identify statistically significant numerical features in the...</td>\n",
-              "      <td id=\"T_fe4d0_row37_col3\" class=\"data row37 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row37_col4\" class=\"data row37 col4\" >{'features': None, 'p_threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row38_col0\" class=\"data row38 col0\" >validmind.data_validation.TargetRateBarPlots</td>\n",
-              "      <td id=\"T_fe4d0_row38_col1\" class=\"data row38 col1\" >Target Rate Bar Plots</td>\n",
-              "      <td id=\"T_fe4d0_row38_col2\" class=\"data row38 col2\" >Generates bar plots visualizing the default rates of categorical features for a classification machine learning...</td>\n",
-              "      <td id=\"T_fe4d0_row38_col3\" class=\"data row38 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row38_col4\" class=\"data row38 col4\" >{'default_column': None, 'columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row39_col0\" class=\"data row39 col0\" >validmind.data_validation.PearsonCorrelationMatrix</td>\n",
-              "      <td id=\"T_fe4d0_row39_col1\" class=\"data row39 col1\" >Pearson Correlation Matrix</td>\n",
-              "      <td id=\"T_fe4d0_row39_col2\" class=\"data row39 col2\" >Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....</td>\n",
-              "      <td id=\"T_fe4d0_row39_col3\" class=\"data row39 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row39_col4\" class=\"data row39 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row40_col0\" class=\"data row40 col0\" >validmind.data_validation.FeatureTargetCorrelationPlot</td>\n",
-              "      <td id=\"T_fe4d0_row40_col1\" class=\"data row40 col1\" >Feature Target Correlation Plot</td>\n",
-              "      <td id=\"T_fe4d0_row40_col2\" class=\"data row40 col2\" >Visualizes the correlation between input features and model's target output in a color-coded horizontal bar plot....</td>\n",
-              "      <td id=\"T_fe4d0_row40_col3\" class=\"data row40 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row40_col4\" class=\"data row40 col4\" >{'features': None, 'fig_height': 600}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row41_col0\" class=\"data row41 col0\" >validmind.data_validation.TabularNumericalHistograms</td>\n",
-              "      <td id=\"T_fe4d0_row41_col1\" class=\"data row41 col1\" >Tabular Numerical Histograms</td>\n",
-              "      <td id=\"T_fe4d0_row41_col2\" class=\"data row41 col2\" >Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...</td>\n",
-              "      <td id=\"T_fe4d0_row41_col3\" class=\"data row41 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row41_col4\" class=\"data row41 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row42_col0\" class=\"data row42 col0\" >validmind.data_validation.IsolationForestOutliers</td>\n",
-              "      <td id=\"T_fe4d0_row42_col1\" class=\"data row42 col1\" >Isolation Forest Outliers</td>\n",
-              "      <td id=\"T_fe4d0_row42_col2\" class=\"data row42 col2\" >Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....</td>\n",
-              "      <td id=\"T_fe4d0_row42_col3\" class=\"data row42 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row42_col4\" class=\"data row42 col4\" >{'random_state': 0, 'contamination': 0.1, 'features_columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row43_col0\" class=\"data row43 col0\" >validmind.data_validation.ChiSquaredFeaturesTable</td>\n",
-              "      <td id=\"T_fe4d0_row43_col1\" class=\"data row43 col1\" >Chi Squared Features Table</td>\n",
-              "      <td id=\"T_fe4d0_row43_col2\" class=\"data row43 col2\" >Executes Chi-Squared test for each categorical feature against a target column to assess significant association....</td>\n",
-              "      <td id=\"T_fe4d0_row43_col3\" class=\"data row43 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row43_col4\" class=\"data row43 col4\" >{'cat_features': None, 'p_threshold': 0.05}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row44_col0\" class=\"data row44 col0\" >validmind.data_validation.HighCardinality</td>\n",
-              "      <td id=\"T_fe4d0_row44_col1\" class=\"data row44 col1\" >High Cardinality</td>\n",
-              "      <td id=\"T_fe4d0_row44_col2\" class=\"data row44 col2\" >Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....</td>\n",
-              "      <td id=\"T_fe4d0_row44_col3\" class=\"data row44 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row44_col4\" class=\"data row44 col4\" >{'num_threshold': 100, 'percent_threshold': 0.1, 'threshold_type': 'percent'}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row45_col0\" class=\"data row45 col0\" >validmind.data_validation.MissingValues</td>\n",
-              "      <td id=\"T_fe4d0_row45_col1\" class=\"data row45 col1\" >Missing Values</td>\n",
-              "      <td id=\"T_fe4d0_row45_col2\" class=\"data row45 col2\" >Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row45_col3\" class=\"data row45 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row45_col4\" class=\"data row45 col4\" >{'min_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row46_col0\" class=\"data row46 col0\" >validmind.data_validation.TabularDescriptionTables</td>\n",
-              "      <td id=\"T_fe4d0_row46_col1\" class=\"data row46 col1\" >Tabular Description Tables</td>\n",
-              "      <td id=\"T_fe4d0_row46_col2\" class=\"data row46 col2\" >Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....</td>\n",
-              "      <td id=\"T_fe4d0_row46_col3\" class=\"data row46 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row46_col4\" class=\"data row46 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row47_col0\" class=\"data row47 col0\" >validmind.data_validation.UniqueRows</td>\n",
-              "      <td id=\"T_fe4d0_row47_col1\" class=\"data row47 col1\" >Unique Rows</td>\n",
-              "      <td id=\"T_fe4d0_row47_col2\" class=\"data row47 col2\" >Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....</td>\n",
-              "      <td id=\"T_fe4d0_row47_col3\" class=\"data row47 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row47_col4\" class=\"data row47 col4\" >{'min_percent_threshold': 1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row48_col0\" class=\"data row48 col0\" >validmind.data_validation.TooManyZeroValues</td>\n",
-              "      <td id=\"T_fe4d0_row48_col1\" class=\"data row48 col1\" >Too Many Zero Values</td>\n",
-              "      <td id=\"T_fe4d0_row48_col2\" class=\"data row48 col2\" >Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...</td>\n",
-              "      <td id=\"T_fe4d0_row48_col3\" class=\"data row48 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row48_col4\" class=\"data row48 col4\" >{'max_percent_threshold': 0.03}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row49_col0\" class=\"data row49 col0\" >validmind.data_validation.HighPearsonCorrelation</td>\n",
-              "      <td id=\"T_fe4d0_row49_col1\" class=\"data row49 col1\" >High Pearson Correlation</td>\n",
-              "      <td id=\"T_fe4d0_row49_col2\" class=\"data row49 col2\" >Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....</td>\n",
-              "      <td id=\"T_fe4d0_row49_col3\" class=\"data row49 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row49_col4\" class=\"data row49 col4\" >{'max_threshold': 0.3}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row50_col0\" class=\"data row50 col0\" >validmind.data_validation.BivariateHistograms</td>\n",
-              "      <td id=\"T_fe4d0_row50_col1\" class=\"data row50 col1\" >Bivariate Histograms</td>\n",
-              "      <td id=\"T_fe4d0_row50_col2\" class=\"data row50 col2\" >Generates bivariate histograms for paired features, aiding in visual inspection of categorical variables'...</td>\n",
-              "      <td id=\"T_fe4d0_row50_col3\" class=\"data row50 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row50_col4\" class=\"data row50 col4\" >{'features_pairs': None, 'target_filter': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row51_col0\" class=\"data row51 col0\" >validmind.data_validation.WOEBinTable</td>\n",
-              "      <td id=\"T_fe4d0_row51_col1\" class=\"data row51 col1\" >WOE Bin Table</td>\n",
-              "      <td id=\"T_fe4d0_row51_col2\" class=\"data row51 col2\" >Calculates and assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature in a ML model....</td>\n",
-              "      <td id=\"T_fe4d0_row51_col3\" class=\"data row51 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row51_col4\" class=\"data row51 col4\" >{'breaks_adj': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row52_col0\" class=\"data row52 col0\" >validmind.data_validation.HeatmapFeatureCorrelations</td>\n",
-              "      <td id=\"T_fe4d0_row52_col1\" class=\"data row52 col1\" >Heatmap Feature Correlations</td>\n",
-              "      <td id=\"T_fe4d0_row52_col2\" class=\"data row52 col2\" >Creates a heatmap to visually represent correlation patterns between pairs of numerical features in a dataset....</td>\n",
-              "      <td id=\"T_fe4d0_row52_col3\" class=\"data row52 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row52_col4\" class=\"data row52 col4\" >{'declutter': None, 'fontsize': None, 'num_features': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row53_col0\" class=\"data row53 col0\" >validmind.data_validation.DatasetSplit</td>\n",
-              "      <td id=\"T_fe4d0_row53_col1\" class=\"data row53 col1\" >Dataset Split</td>\n",
-              "      <td id=\"T_fe4d0_row53_col2\" class=\"data row53 col2\" >Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...</td>\n",
-              "      <td id=\"T_fe4d0_row53_col3\" class=\"data row53 col3\" >['datasets']</td>\n",
-              "      <td id=\"T_fe4d0_row53_col4\" class=\"data row53 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row54_col0\" class=\"data row54 col0\" >validmind.data_validation.BivariateScatterPlots</td>\n",
-              "      <td id=\"T_fe4d0_row54_col1\" class=\"data row54 col1\" >Bivariate Scatter Plots</td>\n",
-              "      <td id=\"T_fe4d0_row54_col2\" class=\"data row54 col2\" >Generates bivariate scatterplots to visually inspect relationships between pairs of predictor variables in machine...</td>\n",
-              "      <td id=\"T_fe4d0_row54_col3\" class=\"data row54 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row54_col4\" class=\"data row54 col4\" >{'selected_columns': None}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row55_col0\" class=\"data row55 col0\" >validmind.data_validation.WOEBinPlots</td>\n",
-              "      <td id=\"T_fe4d0_row55_col1\" class=\"data row55 col1\" >WOE Bin Plots</td>\n",
-              "      <td id=\"T_fe4d0_row55_col2\" class=\"data row55 col2\" >Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...</td>\n",
-              "      <td id=\"T_fe4d0_row55_col3\" class=\"data row55 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row55_col4\" class=\"data row55 col4\" >{'breaks_adj': None, 'fig_height': 600, 'fig_width': 500}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row56_col0\" class=\"data row56 col0\" >validmind.data_validation.ClassImbalance</td>\n",
-              "      <td id=\"T_fe4d0_row56_col1\" class=\"data row56 col1\" >Class Imbalance</td>\n",
-              "      <td id=\"T_fe4d0_row56_col2\" class=\"data row56 col2\" >Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....</td>\n",
-              "      <td id=\"T_fe4d0_row56_col3\" class=\"data row56 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row56_col4\" class=\"data row56 col4\" >{'min_percent_threshold': 10}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row57_col0\" class=\"data row57 col0\" >validmind.data_validation.IQROutliersBarPlot</td>\n",
-              "      <td id=\"T_fe4d0_row57_col1\" class=\"data row57 col1\" >IQR Outliers Bar Plot</td>\n",
-              "      <td id=\"T_fe4d0_row57_col2\" class=\"data row57 col2\" >Visualizes outlier distribution across percentiles in numerical data using Interquartile Range (IQR) method....</td>\n",
-              "      <td id=\"T_fe4d0_row57_col3\" class=\"data row57 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row57_col4\" class=\"data row57 col4\" >{'threshold': 1.5, 'num_features': None, 'fig_width': 800}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fe4d0_row58_col0\" class=\"data row58 col0\" >validmind.data_validation.TabularDateTimeHistograms</td>\n",
-              "      <td id=\"T_fe4d0_row58_col1\" class=\"data row58 col1\" >Tabular Date Time Histograms</td>\n",
-              "      <td id=\"T_fe4d0_row58_col2\" class=\"data row58 col2\" >Generates histograms to provide graphical insight into the distribution of time intervals in model's datetime data....</td>\n",
-              "      <td id=\"T_fe4d0_row58_col3\" class=\"data row58 col3\" >['dataset']</td>\n",
-              "      <td id=\"T_fe4d0_row58_col4\" class=\"data row58 col4\" >None</td>\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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x3621c76d0>"
+              "<pandas.io.formats.style.Styler at 0x103a27bb0>"
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           },
           "execution_count": 7,
@@ -2290,64 +3321,94 @@
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-              "      <th id=\"T_fee5a_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\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",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_fee5a_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_fee5a_row0_col1\" class=\"data row0 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_fee5a_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_fee5a_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fee5a_row0_col4\" class=\"data row0 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fee5a_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_fee5a_row1_col1\" class=\"data row1 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_fee5a_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_fee5a_row1_col3\" class=\"data row1 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fee5a_row1_col4\" class=\"data row1 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fee5a_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_fee5a_row2_col1\" class=\"data row2 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_fee5a_row2_col2\" class=\"data row2 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_fee5a_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_fee5a_row2_col4\" class=\"data row2 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fee5a_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_fee5a_row3_col1\" class=\"data row3 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_fee5a_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_fee5a_row3_col3\" class=\"data row3 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fee5a_row3_col4\" class=\"data row3 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_fee5a_row4_col0\" class=\"data row4 col0\" >validmind.model_validation.statsmodels.GINITable</td>\n",
-              "      <td id=\"T_fee5a_row4_col1\" class=\"data row4 col1\" >GINI Table</td>\n",
-              "      <td id=\"T_fee5a_row4_col2\" class=\"data row4 col2\" >Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....</td>\n",
-              "      <td id=\"T_fee5a_row4_col3\" class=\"data row4 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_fee5a_row4_col4\" class=\"data row4 col4\" >None</td>\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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x360049c90>"
+              "<pandas.io.formats.style.Styler at 0x37563ceb0>"
             ]
           },
           "execution_count": 8,
@@ -2375,57 +3436,85 @@
           "data": {
             "text/html": [
               "<style type=\"text/css\">\n",
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+              "#T_6455a th {\n",
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-              "#T_19b9c_row0_col0, #T_19b9c_row0_col1, #T_19b9c_row0_col2, #T_19b9c_row0_col3, #T_19b9c_row0_col4, #T_19b9c_row1_col0, #T_19b9c_row1_col1, #T_19b9c_row1_col2, #T_19b9c_row1_col3, #T_19b9c_row1_col4, #T_19b9c_row2_col0, #T_19b9c_row2_col1, #T_19b9c_row2_col2, #T_19b9c_row2_col3, #T_19b9c_row2_col4, #T_19b9c_row3_col0, #T_19b9c_row3_col1, #T_19b9c_row3_col2, #T_19b9c_row3_col3, #T_19b9c_row3_col4 {\n",
+              "#T_6455a_row0_col0, #T_6455a_row0_col1, #T_6455a_row0_col2, #T_6455a_row0_col3, #T_6455a_row0_col4, #T_6455a_row0_col5, #T_6455a_row0_col6, #T_6455a_row1_col0, #T_6455a_row1_col1, #T_6455a_row1_col2, #T_6455a_row1_col3, #T_6455a_row1_col4, #T_6455a_row1_col5, #T_6455a_row1_col6, #T_6455a_row2_col0, #T_6455a_row2_col1, #T_6455a_row2_col2, #T_6455a_row2_col3, #T_6455a_row2_col4, #T_6455a_row2_col5, #T_6455a_row2_col6, #T_6455a_row3_col0, #T_6455a_row3_col1, #T_6455a_row3_col2, #T_6455a_row3_col3, #T_6455a_row3_col4, #T_6455a_row3_col5, #T_6455a_row3_col6, #T_6455a_row4_col0, #T_6455a_row4_col1, #T_6455a_row4_col2, #T_6455a_row4_col3, #T_6455a_row4_col4, #T_6455a_row4_col5, #T_6455a_row4_col6, #T_6455a_row5_col0, #T_6455a_row5_col1, #T_6455a_row5_col2, #T_6455a_row5_col3, #T_6455a_row5_col4, #T_6455a_row5_col5, #T_6455a_row5_col6 {\n",
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               "}\n",
               "</style>\n",
-              "<table id=\"T_19b9c\">\n",
+              "<table id=\"T_6455a\">\n",
               "  <thead>\n",
               "    <tr>\n",
-              "      <th id=\"T_19b9c_level0_col0\" class=\"col_heading level0 col0\" >ID</th>\n",
-              "      <th id=\"T_19b9c_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
-              "      <th id=\"T_19b9c_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-              "      <th id=\"T_19b9c_level0_col3\" class=\"col_heading level0 col3\" >Required Inputs</th>\n",
-              "      <th id=\"T_19b9c_level0_col4\" class=\"col_heading level0 col4\" >Params</th>\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",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <td id=\"T_19b9c_row0_col0\" class=\"data row0 col0\" >validmind.model_validation.sklearn.ConfusionMatrix</td>\n",
-              "      <td id=\"T_19b9c_row0_col1\" class=\"data row0 col1\" >Confusion Matrix</td>\n",
-              "      <td id=\"T_19b9c_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_19b9c_row0_col3\" class=\"data row0 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_19b9c_row0_col4\" class=\"data row0 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_19b9c_row1_col0\" class=\"data row1 col0\" >validmind.model_validation.sklearn.PrecisionRecallCurve</td>\n",
-              "      <td id=\"T_19b9c_row1_col1\" class=\"data row1 col1\" >Precision Recall Curve</td>\n",
-              "      <td id=\"T_19b9c_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_19b9c_row1_col3\" class=\"data row1 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_19b9c_row1_col4\" class=\"data row1 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_19b9c_row2_col0\" class=\"data row2 col0\" >validmind.model_validation.sklearn.ROCCurve</td>\n",
-              "      <td id=\"T_19b9c_row2_col1\" class=\"data row2 col1\" >ROC Curve</td>\n",
-              "      <td id=\"T_19b9c_row2_col2\" class=\"data row2 col2\" >Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...</td>\n",
-              "      <td id=\"T_19b9c_row2_col3\" class=\"data row2 col3\" >['model', 'dataset']</td>\n",
-              "      <td id=\"T_19b9c_row2_col4\" class=\"data row2 col4\" >None</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td id=\"T_19b9c_row3_col0\" class=\"data row3 col0\" >validmind.model_validation.sklearn.TrainingTestDegradation</td>\n",
-              "      <td id=\"T_19b9c_row3_col1\" class=\"data row3 col1\" >Training Test Degradation</td>\n",
-              "      <td id=\"T_19b9c_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_19b9c_row3_col3\" class=\"data row3 col3\" >['model', 'datasets']</td>\n",
-              "      <td id=\"T_19b9c_row3_col4\" class=\"data row3 col4\" >{'metrics': ['accuracy', 'precision', 'recall', 'f1'], 'max_threshold': 0.1}</td>\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",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n"
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x17f1863d0>"
+              "<pandas.io.formats.style.Styler at 0x375608640>"
             ]
           },
           "execution_count": 9,
@@ -2456,46 +3545,46 @@
         {
           "data": {
             "text/plain": [
-              "['validmind.prompt_validation.Bias',\n",
-              " 'validmind.prompt_validation.Clarity',\n",
-              " 'validmind.prompt_validation.Specificity',\n",
-              " 'validmind.prompt_validation.Robustness',\n",
-              " 'validmind.prompt_validation.NegativeInstruction',\n",
-              " 'validmind.prompt_validation.Conciseness',\n",
-              " 'validmind.prompt_validation.Delimitation',\n",
+              "['validmind.data_validation.DatasetDescription',\n",
+              " 'validmind.data_validation.DatasetSplit',\n",
+              " 'validmind.data_validation.nlp.CommonWords',\n",
+              " 'validmind.data_validation.nlp.Hashtags',\n",
+              " 'validmind.data_validation.nlp.LanguageDetection',\n",
+              " 'validmind.data_validation.nlp.Mentions',\n",
+              " 'validmind.data_validation.nlp.Punctuations',\n",
+              " 'validmind.data_validation.nlp.StopWords',\n",
+              " 'validmind.data_validation.nlp.TextDescription',\n",
               " 'validmind.model_validation.BertScore',\n",
-              " 'validmind.model_validation.RegardScore',\n",
               " 'validmind.model_validation.BleuScore',\n",
               " 'validmind.model_validation.ContextualRecall',\n",
               " 'validmind.model_validation.MeteorScore',\n",
+              " 'validmind.model_validation.RegardScore',\n",
               " 'validmind.model_validation.RougeScore',\n",
-              " 'validmind.model_validation.ModelMetadata',\n",
               " 'validmind.model_validation.TokenDisparity',\n",
               " 'validmind.model_validation.ToxicityScore',\n",
               " 'validmind.model_validation.embeddings.CosineSimilarityComparison',\n",
-              " 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',\n",
-              " 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',\n",
               " 'validmind.model_validation.embeddings.CosineSimilarityHeatmap',\n",
               " 'validmind.model_validation.embeddings.EuclideanDistanceComparison',\n",
               " 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap',\n",
-              " 'validmind.model_validation.ragas.ContextEntityRecall',\n",
-              " 'validmind.model_validation.ragas.Faithfulness',\n",
-              " 'validmind.model_validation.ragas.AspectCritique',\n",
-              " 'validmind.model_validation.ragas.AnswerSimilarity',\n",
+              " 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',\n",
+              " 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',\n",
               " 'validmind.model_validation.ragas.AnswerCorrectness',\n",
-              " 'validmind.model_validation.ragas.ContextRecall',\n",
-              " 'validmind.model_validation.ragas.ContextRelevancy',\n",
+              " 'validmind.model_validation.ragas.AspectCritic',\n",
+              " 'validmind.model_validation.ragas.ContextEntityRecall',\n",
               " 'validmind.model_validation.ragas.ContextPrecision',\n",
-              " 'validmind.model_validation.ragas.AnswerRelevance',\n",
-              " 'validmind.data_validation.DatasetDescription',\n",
-              " 'validmind.data_validation.DatasetSplit',\n",
-              " 'validmind.data_validation.nlp.Punctuations',\n",
-              " 'validmind.data_validation.nlp.CommonWords',\n",
-              " 'validmind.data_validation.nlp.Hashtags',\n",
-              " 'validmind.data_validation.nlp.LanguageDetection',\n",
-              " 'validmind.data_validation.nlp.Mentions',\n",
-              " 'validmind.data_validation.nlp.TextDescription',\n",
-              " 'validmind.data_validation.nlp.StopWords']"
+              " 'validmind.model_validation.ragas.ContextPrecisionWithoutReference',\n",
+              " 'validmind.model_validation.ragas.ContextRecall',\n",
+              " 'validmind.model_validation.ragas.Faithfulness',\n",
+              " 'validmind.model_validation.ragas.NoiseSensitivity',\n",
+              " 'validmind.model_validation.ragas.ResponseRelevancy',\n",
+              " 'validmind.model_validation.ragas.SemanticSimilarity',\n",
+              " 'validmind.prompt_validation.Bias',\n",
+              " 'validmind.prompt_validation.Clarity',\n",
+              " 'validmind.prompt_validation.Conciseness',\n",
+              " 'validmind.prompt_validation.Delimitation',\n",
+              " 'validmind.prompt_validation.NegativeInstruction',\n",
+              " 'validmind.prompt_validation.Robustness',\n",
+              " 'validmind.prompt_validation.Specificity']"
             ]
           },
           "execution_count": 10,
@@ -2527,12 +3616,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "571210f026b14522a043157e2c9b708e",
+              "model_id": "5025f3a7dbb34f4c9de1b26e4909f3f7",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Accordion(children=(HTML(value='\\n<div>\\n  <h2>Overfit Diagnosis</h2>\\n  <p>Detects and visualizes overfit reg…"
+              "Accordion(children=(HTML(value='\\n<div>\\n  <h2>Overfit Diagnosis</h2>\\n  <div style=\"border: 1px solid #ddd; b…"
             ]
           },
           "metadata": {},
@@ -2617,7 +3706,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "validmind-1QuffXMV-py3.9",
+      "display_name": ".venv",
       "language": "python",
       "name": "python3"
     },
@@ -2631,7 +3720,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.6"
+      "version": "3.9.20"
     }
   },
   "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index eb0d90a62..a7bbd54df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.8.15"
+version = "2.8.16"
 
 [tool.poetry.dependencies]
 aiohttp = {extras = ["speedups"], version = "*"}
diff --git a/validmind/__version__.py b/validmind/__version__.py
index b365dbcd9..070a948f5 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.8.15"
+__version__ = "2.8.16"
diff --git a/validmind/tests/data_validation/ClassImbalance.py b/validmind/tests/data_validation/ClassImbalance.py
index bab192e74..14d4eacf3 100644
--- a/validmind/tests/data_validation/ClassImbalance.py
+++ b/validmind/tests/data_validation/ClassImbalance.py
@@ -14,7 +14,9 @@
 from validmind.vm_models import VMDataset
 
 
-@tags("tabular_data", "binary_classification", "multiclass_classification")
+@tags(
+    "tabular_data", "binary_classification", "multiclass_classification", "data_quality"
+)
 @tasks("classification")
 def ClassImbalance(
     dataset: VMDataset, min_percent_threshold: int = 10
diff --git a/validmind/tests/data_validation/DescriptiveStatistics.py b/validmind/tests/data_validation/DescriptiveStatistics.py
index 1303a53e5..3e9e929e4 100644
--- a/validmind/tests/data_validation/DescriptiveStatistics.py
+++ b/validmind/tests/data_validation/DescriptiveStatistics.py
@@ -44,7 +44,7 @@ def get_summary_statistics_categorical(df, categorical_fields):
     return summary_stats
 
 
-@tags("tabular_data", "time_series_data")
+@tags("tabular_data", "time_series_data", "data_quality")
 @tasks("classification", "regression")
 def DescriptiveStatistics(dataset: VMDataset):
     """
diff --git a/validmind/tests/load.py b/validmind/tests/load.py
index 6dd41b2c1..697c64219 100644
--- a/validmind/tests/load.py
+++ b/validmind/tests/load.py
@@ -146,12 +146,12 @@ def _load_tests(test_ids: List[str]) -> Dict[str, Callable[..., Any]]:
             logger.debug(str(e))
 
             if e.extra:
-                logger.info(
+                logger.debug(
                     f"Skipping `{test_id}` as it requires extra dependencies: {e.required_dependencies}."
                     f" Please run `pip install validmind[{e.extra}]` to view and run this test."
                 )
             else:
-                logger.info(
+                logger.debug(
                     f"Skipping `{test_id}` as it requires missing dependencies: {e.required_dependencies}."
                     " Please install the missing dependencies to view and run this test."
                 )
@@ -183,6 +183,8 @@ def _pretty_list_tests(
             ),
             "Required Inputs": list(test.inputs.keys()),
             "Params": test.params,
+            "Tags": test.__tags__,
+            "Tasks": test.__tasks__,
         }
         for test_id, test in tests.items()
     ]