SHAP values, or SHapley Additive exPlanations, are a method for interpreting the output of machine learning models by quantifying the contribution of each feature to a given prediction. They provide a unified measure of feature importance that reflects how much each feature influences the predicted outcome, making them especially useful for causal feature selection. By utilizing cooperative game theory principles, SHAP values help to ensure that the contributions of features are fairly distributed, allowing for better understanding and validation of model predictions.
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