In this paper I consider improving the KernelSHAP algorithm. I suggest to use the Wallenius' noncentral hypergeometric distribution for sampling the number of coalitions and perform sampling without replacement, so that the KernelSHAP estimation framework is improved further. I also introduce the Symmetric bootstrap to calculate the standard deviations and also use the Doubled half bootstrap method to compare the performance. The new bootstrap algorithm performs better or equally well in the two simulation studies performed in this paper. The new KernelSHAP algorithm performs similarly as the improved KernelSHAP method in the state-of-the-art R-package shapr, which samples coalitions with replacement in one of the options

翻译：本文旨在改进KernelSHAP算法。我建议采用Wallenius非中心超几何分布来确定联盟数量，并实施无放回抽样策略，从而进一步完善KernelSHAP估计框架。同时，我引入了对称自助法用于计算标准差，并采用双半自助法进行性能比较。在本文开展的两项模拟研究中，新提出的自助算法均表现出更优或相当的性能。新KernelSHAP算法与当前先进的R软件包shapr中经过改进的KernelSHAP方法（该方法的可选方案之一采用有放回联盟抽样）具有相似的表现。