WoeBoost

Author: xRiskLab
Version: v1.0.1
License: MIT License (2024)

WoeBoost is a Python 🐍 package designed to bridge the gap between the predictive power of gradient boosting and the interpretability required in high-stakes domains such as finance, healthcare, and law. It introduces an interpretable, evidence-driven framework for scoring tasks, inspired by the principles of Weight of Evidence (WOE) and the ideas of Alan M. Turing.

🔑 Key Features

• 🌟 Gradient Boosting with Explainability: Combines the strength of gradient boosting with the interpretability of WOE-based scoring systems.
• 📊 Calibrated Scores: Produces well-calibrated scores essential for decision-making in regulated environments.
• 🤖 AutoML-like Enhancements:
  • Infers monotonic relationships automatically (infer_monotonicity).
  • Supports early stopping for efficient training (enable_early_stopping).
• 🔧 Support for Missing Values & Categorical Inputs: Handles various data types seamlessly while maintaining interpretability.
• 🛠️ Diagnostic Toolkit:
  • Partial dependence plots.
  • Feature importance analysis.
  • Decision boundary visualization.
• 📈 WOE Inference Maker: Provides classical WOE calculations and bin-level insights.

⚙️ How It Works

1. 🔍 Initialization: Starts with prior log odds, representing baseline probabilities.
2. 📈 Iterative Updates: Each boosting iteration calculates residual per each binned feature and sums residuals into total evidence (WOE), updating predictions.
3. 🔗 Evidence Accumulation: Combines evidence from all iterations, producing a cumulative and interpretable scoring model.

🧐 Why WoeBoost?

• 💡 Interpretability: Every model step adheres to principles familiar to risk managers and data scientists, ensuring transparency and trust.
• ✅ Alignment with Regulatory Requirements: Calibrated and interpretable results meet the demands of high-stakes applications.
• ⚡ Flexibility: Works seamlessly with diverse data types and supports customizations for complex datasets, including multi-threading for CPU.

Installation ⤵

Install the package using pip:

pip install woeboost

💻 Example Usage

Below we provide two examples of using WoeBoost.

Training and Inference with WoeBoost classifier

from woeboost import WoeBoostClassifier

# Initialize the classifier
woe_model = WoeBoostClassifier(infer_monotonicity=True)

# Fit the model
woe_model.fit(X_train, y_train)

# Predict probabilities and scores
probas = woe_model.predict_proba(X_test)[:, 1]
preds = woe_model.predict(X_test)
scores = woe_model.predict_score(X_test)

Preparation of WOE inputs for logistic regression

from woeboost import WoeBoostClassifier

# Initialize the classifier
woe_model = WoeBoostClassifier(infer_monotonicity=True)

# Fit the model
woe_model.fit(X_train, y_train)

X_woe_train = woe_model.transform(X_train)
X_woe_test = woe_model.transform(X_test)

📚 Documentation

• Technical Note: Overview of the WoeBoost modules.
• learner.py: Core module implementing a base learner.
• classifier.py: Module for building a boosted classification model.
• explainer.py: Module for explaining the model predictions.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📃 Change Log

• v1.0.1

  • Adjusted feature importance default plot size and added minor updates of documentation.

• v1.0.0

  • Initial release of WoeBoost.