majiayu000 ML Model Explainer
Explain machine learning model predictions using SHAP and feature importance.
Features
- SHAP Values: Explain individual predictions
- Feature Importance: Global feature rankings
- Decision Paths: Trace prediction logic
- Visualizations: Waterfall, force plots, summary plots
- Multiple Models: Support for tree-based, linear, neural networks
- Batch Explanations: Explain multiple predictions
Quick Start
from ml_model_explainer import MLModelExplainer
explainer = MLModelExplainer()
explainer.load_model(model, X_train)
# Explain single prediction
explanation = explainer.explain(X_test[0])
explainer.plot_waterfall('explanation.png')
# Feature importance
importance = explainer.feature_importance()
CLI Usage
python ml_model_explainer.py --model model.pkl --data test.csv --output explanations/
Dependencies
- shap>=0.42.0
- scikit-learn>=1.3.0
- pandas>=2.0.0
- numpy>=1.24.0
- matplotlib>=3.7.0