# Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

@inproceedings{Covert2021ImprovingKP, title={Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression}, author={Ian Covert and Su-In Lee}, booktitle={AISTATS}, year={2021} }

The Shapley value solution concept from cooperative game theory has become popular for interpreting ML models, but efficiently estimating Shapley values remains challenging, particularly in the model-agnostic setting. We revisit the idea of estimating Shapley values via linear regression to understand and improve upon this approach. By analyzing KernelSHAP alongside a newly proposed unbiased estimator, we develop techniques to detect its convergence and calculate uncertainty estimates. We also…

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