There are now many comprehension algorithms for understanding the decisions of a machine learning algorithm. Among these are those based on the generation of counterfactual examples. This article proposes to view this generation process as a source of creating a certain amount of knowledge that can be stored to be used, later, in different ways. This process is illustrated in the additive model and, more specifically, in the case of the naive Bayes classifier, whose interesting properties for this purpose are shown.

翻译：目前已有多种用于理解机器学习算法决策的算法，其中基于反事实示例生成的方法尤为突出。本文提出将这一生成过程视为能够创造并存储一定量知识的来源，这些知识可在后续以不同方式被利用。该过程在加性模型中加以阐释，并具体应用于朴素贝叶斯分类器，展示了其在此类场景下的显著特性。