Decision trees have long been recognized as models of choice in sensitive applications
where interpretability is of paramount importance. In this paper, we examine the
computational ability of Boolean decision trees for the explanation purpose. We focus on
both abductive explanations (suited to explaining why a given instance has been classified
as such by the decision tree at hand) and on contrastive explanations (suited to explaining
why a given instance has not been classified by the decision tree as it was expected). More …