SHAP

SHAP (SHapley Additive exPlanations) is a method that helps us understand why a machine learning model makes a particular prediction. It assigns each feature (input variable) a value that shows how much it contributed—either positively or negatively—compared to the average prediction. Think of it like breaking down a team's effort to see who contributed most to a project. SHAP provides clear, consistent explanations across different models, enabling us to interpret complex decisions transparently and trust the model's reasoning better.