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.Comment: 12 page