Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

Hui Duan; Giray Ökten

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arXiv · 2303.15183

Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

Hui Duan·Giray Ökten

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Abstract

We introduce a new Shapley value approach for global sensitivity analysis and machine learning explainability. The method is based on the first-order partial derivatives of the underlying function. The computational complexity of the method is linear in dimension (number of features), as opposed to the exponential complexity of other Shapley value approaches in the literature. Examples from global sensitivity analysis and machine learning are used to compare the method numerically with activity scores, SHAP, and KernelSHAP.

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Hui Duan, Giray Ökten. 2023-03-24. Derivative-based Shapley value for global sensitivity analysis and machine learning explainability. https://arxiv.org/abs/2303.15183

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