CHARLIE (Combined Alpha-weighted Random Forest Layered Inference Ensemble)

CHARLIE is an acronym that encapsulates the core process of this model. Standing for:

• Combined: blending two modeling techniques (Random Forest & Neural Networks)
• Alpha-weighted: the learnable parameter that controls the blending $a$
• Random Forest: used for feature extraction
• Layered: the structure of the neural network contains multiple layers
• Inference Ensemble: Final predictive ensemble combining RF and NN outputs.

Why it is really called CHARLIE? I am sure only my son knows that ❤️.

Importing CHARLIE to perform ensembling

To import the package we go to the below:

pip install charliepy

This will get the project from PyPi: and then you can import the model using:

from charlie.models.ensemble import CHARLIE

Overview

The CHARLIE class implements a hybrid ML model that combines:

• Random Forest (RF) for feature importance ranking and initial predictions
• Feedforward Neural Network (NN) for learning non-linear relationships on selected top features
• Learnable weighting parameter that blends predictions from both models

Model architecture

Consists of two models:

• Random Forest trained on the entire feature set and outputs either class probs or continuous predictions.
• Neural Network - built after using a reduced features set based on RF feature importance

Training Process

1. Random Forest Training:

   • Trained on full feature set (all our $X$ features)
   • Outputs the importance $I$ of each feature i.e. how much each feature affects the prediction

2. Feature Selection:

   • Select top selected_features based on their importance $I$

3. Neural Network Building:

   • NN input dimension is those selected features
   • These are configured according to the number of hidden_layers passed as a Tuple to the Neural Network

4. Neural Network Training:

• Loss Function:

  • Classification: Cross Entropy Loss (https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)

  • Regression: Mean Squared Error Loss (https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html)

• Optimiser: ADAM (https://pytorch.org/docs/stable/generated/torch.optim.Adam.html)

• Training updates both:

  • NN weights $\theta{}_{NN}$
  • Blending parameter $\alpha$

Mathematical Formulation Summary

$$\hat{\mathbf{y}} = \alpha\cdot f_\text{RF}(\mathbf{X})+(1-\alpha) \cdot f_\text{NN}(\mathbf{X}_\text{top})$$

where:

• $\alpha$ is trained alongside $\text{NN}$ parameters
• $f_\text{RF}$ is trained first

How to use CHARLIE?

The first step, we will gather the imports that we need:

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, f1_score
from sklearn.model_selection import train_test_split
from charlie.models.ensemble import CHARLIE

Preprocess data

The next stage is to preprocess the heart disease classification data we are going to need to use:

# Load and preprocess data
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"
columns = [
    "age", "sex", "cp", "trestbps", "chol", "fbs", "restecg",
    "thalach", "exang", "oldpeak", "slope", "ca", "thal", "target"
]
df = pd.read_csv(url, names=columns)
df.replace('?', np.nan, inplace=True)
df.dropna(inplace=True)
df['ca'] = df['ca'].astype(float)
df['thal'] = df['thal'].astype(float)
df["target"] = (df["target"].astype(int) > 0).astype(int)
X = df.drop(columns=['target']).astype(float).values
y = df['target'].values

Split and scale

We will now split the data ino training and testing splits, ready to be used:

# Split our data into train and test splits
X_train, X_test, y_train, y_test = train_test_split(
    X, y, random_state=42, test_size=0.2
)

# Scale features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Evaluation step

In this step, we will create an evaluation function for the project:

def evaluate_model(name, model, X_train, y_train, X_test, y_test):
    """
    Function to use accuracy and F1 score as our measures
    """
    model.fit(X_train, y_train)
    preds = model.predict(X_test)
    acc = accuracy_score(y_test, preds)
    f1 = f1_score(y_test, preds)
    print(f"{name} - Accuracy: {acc:.4f}, F1-score: {f1:.4f}")
    return {"Model": name, "Accuracy": acc, "F1-score": f1}

Modelling with our baseline models

We will use a Logistic Regression, Random Forest and Boosted Forest (XGBoost) to prepare our comparisons:

results = []
print("=== Traditional Models ===")
models = {
    "Logistic Regression": LogisticRegression(max_iter=200),
    "Random Forest": RandomForestClassifier(n_estimators=100, random_state=42),
    "XGBoost": XGBClassifier(use_label_encoder=False)
}

for name, model in models.items():
    res = evaluate_model(name, model, X_train, y_train, X_test, y_test)
    results.append(res)

The loop at the end iterates through the model versions and finds appends the evaluated model results to the empty list.

Using CHARLIE

In this step, we will use CHARLIE to do the training:

charlie = CHARLIE(
    input_dim=X_train.shape[1],
    selected_features=6, 
    rf_trees=100,
    hidden_layers=(128, 64, 32),
    classification=True
)
charlie.train_model(X_train, y_train, epochs=50, lr=0.001)

The model will train, do the feature selection and then train the network, as outlined in the training section above.

Once trained, we can use the instantiated class to reveal the predict class method, this will be useful for using against our test set:

charlie_preds = charlie.predict(X_test)
charlie_preds_binary = np.argmax(charlie_preds, axis=1

Now we have the predictions, we will use the same metrics and append our results from the CHARLIE model and then do a model comparison:

acc = accuracy_score(y_test, charlie_preds_binary)
f1 = f1_score(y_test, charlie_preds_binary)
print(f"CHARLIE - Accuracy: {acc:.4f}, F1-score: {f1:.4f}")
results.append({"Model": "CHARLIE", "Accuracy": acc, "F1-score": f1})

# Store results in DataFrame
results_df = pd.DataFrame(results)
results_df.sort_values(
    by="F1-score", 
    ascending=False).to_string(index=False)

Compare CHARLIE to baseline models

The following visualisation will compare the CHARLIE model to the baseline models we chose:

import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.bar(results_df['Model'], 
        results_df['Accuracy'], 
        alpha=0.6, label='Accuracy')
plt.plot(results_df['Model'], 
         results_df['F1-score'], 
         color='red', 
         marker='o', 
         label='F1-score')
plt.title('Model Performance Comparison')
plt.xlabel('Model')
plt.ylabel('Score')
plt.ylim(0, 1)
plt.legend()
plt.grid(True, linestyle='--', alpha=0.6)
plt.show()

This produces the visualisation illustrated below:

Due to combining our feature selector with a neural network, we can beat the standard Random Forest classifier on its own, as well as XGBoost, which shows the power of this approach, as accuracy=0.9 and F1-Score=0.869.

Cross-Validation with MultiModelCrossValidator

The MultiModelCrossValidator is a utility for performing cross-validation on multiple models with custom hyperparameters. It supports various metrics, robust error handling, and flexible progress modes.

Usage Example

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import roc_auc_score

data = load_breast_cancer()
X, y = data.data, data.target

models = [
    ("RandomForest", RandomForestClassifier, {"n_estimators": 100}),
    ("GradientBoosting", GradientBoostingClassifier, {"n_estimators": 200})
]

validator = MultiModelCrossValidator(
    models=models,
    score_fn=roc_auc_score,
    higher_is_better=True,
    cv_splits=5,
    use_tqdm=True,
    progress_mode="model",
    threshold=0.4   # custom threshold for label-based metrics
)

results = validator.cross_validate(X, y)
print("Best model:", validator.best_model_info)
predictions = validator.predict_proba(X[:5])

Ensembling Methods in CHARLIE

The CHARLIE package provides several ensembling methods to combine predictions from multiple models. These methods can improve the robustness and accuracy of predictions by leveraging the strengths of different models.

Example Usage

Below are examples of how to use the ensembling methods available in charlie.utils.

Weighted Ensemble

import numpy as np
from charlie.utils import weighted_ensemble

probs1 = np.array([[0.7, 0.3], [0.4, 0.6]])
probs2 = np.array([[0.6, 0.4], [0.5, 0.5]])
ensemble_probs = weighted_ensemble([probs1, probs2], weights=[0.8, 0.2])
print(ensemble_probs)

Mean Ensemble

from charlie.utils import mean_ensemble

probs1 = np.array([[0.7, 0.3], [0.4, 0.6]])
probs2 = np.array([[0.6, 0.4], [0.5, 0.5]])
ensemble_probs = mean_ensemble([probs1, probs2])
print(ensemble_probs)

Median Ensemble

from charlie.utils import median_ensemble

probs1 = np.array([[0.7, 0.3], [0.4, 0.6]])
probs2 = np.array([[0.6, 0.4], [0.5, 0.5]])
probs3 = np.array([[0.9, 0.1], [0.2, 0.8]])
ensemble_probs = median_ensemble([probs1, probs2, probs3])
print(ensemble_probs)

Max Ensemble

from charlie.utils import max_ensemble

probs1 = np.array([[0.7, 0.3], [0.4, 0.6]])
probs2 = np.array([[0.6, 0.4], [0.5, 0.5]])
ensemble_probs = max_ensemble([probs1, probs2])
print(ensemble_probs)

Rank Ensemble

from charlie.utils import rank_ensemble

probs1 = np.array([[0.7, 0.3], [0.4, 0.6]])
probs2 = np.array([[0.6, 0.4], [0.5, 0.5]])
ensemble_probs = rank_ensemble([probs1, probs2])
print(ensemble_probs)

These examples demonstrate how to use the ensembling functions provided by CHARLIE to combine model predictions effectively.

Feature Selection with CharlieRFECV

The CharlieRFECV class can be used to perform recursive feature elimination with cross-validation to select the most important features for your model.

import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from charlie.feature_selection.rfecv import CharlieRFECV

# Load dataset
data = load_iris()
X, y = data.data, data.target

# Initialize the estimator
estimator = RandomForestClassifier(n_estimators=100, random_state=42)

# Initialize CharlieRFECV
rfecv = CharlieRFECV(estimator=estimator, step=1, cv=5, scoring=None, min_features_to_select=1, verbose=1)

# Fit the model
rfecv.fit(X, y)

# Transform the dataset
X_transformed = rfecv.transform(X)

# Print the best score and selected features
print(f"Best CV Score: {rfecv.best_score_}")
print(f"Selected Features: {np.where(rfecv.best_support_)[0]}")

Performance on regression credit modelling

This was contributed by https://www.linkedin.com/in/venkateshwari-narayanan-668661176/.

🚀 Credit Score Prediction Framework: We recently tested the CHARLIE Model (Combined Alpha-weighted Random Forest Layered Inference Ensemble) on a credit score prediction task and compared its performance against several traditional models. The results were remarkable, demonstrating CHARLIE’s ability to balance accuracy and robustness.

📈 Visualizing Performance Metrics

To further highlight CHARLIE's performance, the following visual comparisons showcase results for key metrics across models chosen for regression:

⚡️ Key Insights:

• 🎯 The CHARLIE Model (Linear) matched the performance of Linear Regression with near-perfect accuracy (R² = 0.9999) while maintaining low error values across MSE, RMSE, and MAE.

• 🎯It demonstrated high stability in cross-validation, with a mean cross-validated MSE of 0.0804 and a minimal standard deviation of 0.0021.

• 🎯 Compared to traditional models like Ridge, Lasso, and Decision Trees, CHARLIE exhibited superior generalization and significantly lower variance.

• 🎯Gradient Boosting also showed strong performance but was slightly outperformed by the CHARLIE Model.

🎯 Potential Applications:

The CHARLIE Model’s exceptional performance makes it well-suited for:

• Financial risk analysis and credit score prediction.

• Regression tasks involving structured, tabular data with complex relationships.

• Datasets where high accuracy and consistency are critical.