Shapley Method

The Shapley method is a game-theoretic approach used to explain the contributions of individual features or data points to a model's prediction or overall performance. Current research focuses on addressing limitations of traditional Shapley methods, such as computational cost and the assumption of feature independence, through techniques like manifold-based approaches and surrogate models, as well as adapting it for probabilistic classifiers and ensuring differential privacy. This work is significant for improving the transparency and trustworthiness of machine learning models, particularly in high-stakes applications like credit scoring and medical diagnosis, by providing more accurate and interpretable explanations.