SHAP (SHapley Additive exPlanations)
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A SHAP (SHapley Additive explanations) is a supervised ML model explanation algorithm that ...
- See: Shapley Value.
References
2020
- https://shap.readthedocs.io/en/latest/
- QUOTE: SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
2017
- (Lundberg & Lee, 2017) ⇒ Scott M. Lundberg, and Su-In Lee. (2017). “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30