论文标题
改进内核变形：实用的沙普利价值通过线性回归估算
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
论文作者
论文摘要
合作游戏理论中的Shapley价值概念已成为解释ML模型的流行技术，但是有效估计这些价值仍然具有挑战性，尤其是在模型不合时宜的环境中。在这里，我们重新审视通过线性回归估算Shapley值的想法，以理解和改进这种方法。通过与新提出的无偏见版本分析原始内核变形，我们开发了检测其收敛性并计算不确定性估计值的技术。我们还发现，原始版本会造成偏见的可忽略不计，以换取较低的差异，我们提出了一种降低差异技术，以进一步加速这两个估计器的收敛性。最后，我们为随机合作游戏开发了一种内核变形版，该版本为两种全球解释方法提供了快速的新估计器。
The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, particularly in the model-agnostic setting. Here, we revisit the idea of estimating Shapley values via linear regression to understand and improve upon this approach. By analyzing the original KernelSHAP alongside a newly proposed unbiased version, we develop techniques to detect its convergence and calculate uncertainty estimates. We also find that the original version incurs a negligible increase in bias in exchange for significantly lower 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.
