SHAP

SHAP (Shapley Additive Explanations) is a feature attribution method grounded in cooperative game theory, specifically utilizing Shapley values to explain the output of machine learning models. The Shapley value provides a fair distribution of payouts among players based on their contributions to the total outcome, which in the context of machine learning translates to quantifying the impact of each feature on a model's prediction. SHAP values are computed by considering all possible combinations of features, leading to a comprehensive understanding of feature importance. This method can be applied to various model types, including tree-based models and neural networks, and is particularly valuable for its consistency and local accuracy. The mathematical formulation involves calculating the expected value of the model's output with and without each feature, thus providing a robust framework for interpretability in AI.