方法证据记录
SHAP
SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).
源记录
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SHAP (SHapley Additive exPlanations)
分类方法记录 · ml-model / machine-learning
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