SHAP Values
SHAP (SHapley Additive exPlanations) values are a unified measure of feature importance in machine learning models. They provide a way to explain the output of any machine learning model by assigning importance values to each feature. SHAP values are based on the concept of Shapley values from cooperative game theory, which assigns a fair distribution of the total payout of a game to each player. In the context of machine learning, the game is the prediction task and the players are the features. SHAP values provide a way to understand how much each feature contributes to the final prediction, and whether that contribution is positive or negative. They can be used to identify which features are most important for a particular prediction, to compare the importance of different features across multiple models, and to detect interactions between features. SHAP values are model-agnostic, meaning they can be applied to any type of machine learning model, including deep learning models.
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