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 find that that the original version incurs a negligible increase in bias in exchange for a significant reduction in variance, and we propose a variance reduction technique that further accelerates the convergence of both estimators. Finally, we develop a version of KernelSHAP for stochastic cooperative games that yields fast new estimators for two global explanation methods.

翻译：合作游戏理论的 " 浅色价值解决方案 " 概念在解释 ML 模型方面已经很受欢迎,但有效估计 " 淡色值 " 仍然具有挑战性,特别是在模型 -- -- 不可知性环境下。我们重新审视通过线性回归估计 " 淡色值 " 的想法,以了解并改进这一方法。我们分析KernelSHAP与新提议的公正估算师一道开发了探测其趋同和计算不确定性估算的技术。我们还发现,原始版本的偏差略有增加,以换取显著减少差异,我们提出了进一步加快两个估计师汇合的减少差异技术。最后,我们开发了KernSHAP的版本,用于为两种全球解释方法产生快速的新估计师。