SHAP method explains machine learning predictions using game theory concept

SHAP, or SHapley Additive exPlanations, helps explain predictions from complex machine learning models. The method is based on Shapley values from cooperative game theory, which fairly distributes contributions among participants. SHAP applies this concept by measuring how much each feature contributes to a specific model prediction. It works by calculating each feature's average marginal contribution across different combinations with other features. This allows users to understand why a model made a particular decision, such as rejecting a loan application.