SHAP Value

SHAP (SHapley Additive exPlanations) values are a game-theoretic approach to interpreting machine learning model predictions by assigning importance scores to individual features. Current research focuses on extending SHAP's applicability to diverse tasks like ranking and continual learning, as well as improving computational efficiency through approximations and surrogate models. This work is significant because it enhances the transparency and trustworthiness of complex models, enabling better understanding of model behavior and facilitating responsible AI development across various applications, including those with societal impact like healthcare and finance.