快速近似计算Shapley值的归因解释方法

Shapley值归因解释方法虽然能更准确量化解释结果, 但过高的计算复杂度严重影响了该方法的实用性. 本文引入KD树重新整理待解释模型的预测数据, 通过在KD树上插入虚节点, 使之满足TreeSHAP算法的使用条件, 在此基础上提出了KDSHAP方法. 该方法解除了TreeSHAP算法仅能解释树结构模型的限制, 将该算法计算Shapley值的高效性放宽到对所有的黑盒模型的解释中, 同时保证了计算准确度. 通过实验对比分析, KDSHAP方法的可靠性, 以及在解释高维输入模型时的适用性.

Attribution Explanation Method for Fast Approximation of Shapley Values

Although the attribution explanation method based on Shapley value can quantify the interpretation results more accurately, the excessive computational complexity seriously affects the practicality of this method. In this study, we introduce the k-dimensional (KD) tree to reorganize the predicted data of the model to be explained, insert virtual nodes into the KD tree so that it meets the application conditions of the TreeSHAP algorithm, and then propose the KDSHAP method. This method lifts the restriction that the TreeSHAP algorithm can only explain tree models and broadens the efficiency of the algorithm in calculating Shapley value to the explanation of all black-box models without compromising calculation accuracy. The reliability of the KDSHAP method and its applicability in interpreting high-dimensional input models are analyzed through experimental comparisons.