Shapley Additive Explanation

Shapley Additive Explanations (SHAP) is a model-agnostic method used to interpret the predictions of machine learning models by assigning importance scores to individual input features. Current research focuses on improving SHAP's computational efficiency, addressing its sensitivity to data imbalances and feature correlations, and integrating it with various model architectures, including tree-based models, neural networks, and reinforcement learning algorithms, across diverse applications. The widespread adoption of SHAP highlights the growing need for explainable AI, enabling greater trust and understanding of complex models in fields ranging from healthcare and finance to engineering and social sciences.