Shapley Additive exPlanations

SHapley Additive exPlanations, commonly known as SHAP, is a tool for making ML models more interpretable. It does this by providing explanations for individual predictions, called Shapley values. SHAP values are different from traditional model interpretation methods such as feature importance because they take into account the interactions between features. This makes them more accurate and reliable, especially in complex models.

SHAP works by approximating the value that each feature contributes to a prediction. This is done using a game-theoretic approach, which assigns each feature a score based on its importance in determining the outcome of the game (prediction). The final SHAP value for a given feature is then calculated as the average of all possible ways that feature could have been included in the prediction. SHAP can be used to explain the output of any ML model. Even though this approach was first proposed by game theorists, it has been...