🔍 Explainable AI (XAI) & Interpretability Research
- SHAP (
shap/shap): A game-theoretic approach to explain the outputs of any machine learning model. It connects optimal credit allocation from cooperative game theory (Shapley values) with local explanations to clearly visualize feature importance.
👉 shap/shap GitHub Repository [1] - InterpretML (
interpretml/interpret): A Microsoft Research initiative that incorporates several state-of-the-art explainable AI algorithms (like Explainable Boosting Machines and LIME) into a single unified framework to train interpretable models.
👉 interpretml/interpret GitHub Repository [1, 2, 3]