SHAP Values

SHAP (SHapley Additive exPlanations) values are a method used to explain the output of machine learning models. They provide a unified measure of feature importance by calculating the contribution of each feature to the prediction made by the model. The method is based on cooperative game theory, specifically the Shapley values, which allocate payouts fairly among players based on their contributions. SHAP values are especially useful in scenarios where interpretability is crucial, such as in finance or healthcare, enabling stakeholders to understand the reasoning behind model predictions. They help in identifying which features are driving the model's decisions, thus promoting transparency and trust in AI systems.