发表机构
Orange Research（橙研）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建加权朴素贝叶斯分类器的解释性空间，证明其诱导的距离与Shapley值向量的ℓ₁距离一致，还通过k近邻分类器实证比较相关监督距离，凸显监督距离、局部解释与预测行为的关联。
AI 中文摘要
本文研究从加权朴素贝叶斯分类器诱导的监督表示出发，构建其解释性空间。我们从基于条件对数似然的经典监督距离入手，引入基于对数几率的判别式重构，该重构与分类决策的关联更为直接。随后证明，该表示诱导的距离与解析Shapley值向量间的ℓ₁距离完全一致，从而为模型诱导的几何提供了正式的解释性解读。最后，我们使用k近邻分类器，对从这些表示中推导的多种监督距离进行实证比较。本研究从方法论视角凸显了监督距离、局部解释与预测行为间的紧密联系。
英文摘要
This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the model. We start from the classical supervised distance based on conditional log-likelihoods and introduce a discriminative reformulation based on log-odds, which is more directly related to the classification decision. We then show that this representation induces a distance that exactly coincides with the $\ell_1$ distance between vectors of analytical Shapley values, thereby providing a formal explanatory interpretation of the geometry induced by the model. Finally, we empirically compare several supervised distances derived from these representations using a $k$-nearest neighbors classifier. This work highlights a close link between supervised distance, local explanation, and predictive behavior, from a primarily methodological perspective.
Comments15 pages
