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A SHapley Additive exPlanation (SHAP) value, whether positive or negative, reflects the contribution of a feature to a predicted response in one datum (Lundberg and Lee, in Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., New York, pp. 4768–4777, 2017). SHAP values are able to rank the feature importance in an additive manner. Consider a data set with m features of \((x_{1} ,\;x_{2} ,\; \cdots ,\;x_{m} )\) and one response \(y = f(x_{1} ,\;x_{2} ,\; \cdots ,\;x_{m} )\) .

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Interpretative SHAP Value and Partial Dependence Plot

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  • 发表日期：

    2025年

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  • Tongyi Zhang

摘要

A SHapley Additive exPlanation (SHAP) value, whether positive or negative, reflects the contribution of a feature to a predicted response in one datum (Lundberg and Lee, in Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., New York, pp. 4768–4777, 2017). SHAP values are able to rank the feature importance in an additive manner. Consider a data set with m features of \((x_{1} ,\;x_{2} ,\; \cdots ,\;x_{m} )\) and one response \(y = f(x_{1} ,\;x_{2} ,\; \cdots ,\;x_{m} )\) .