A contrastive explanation is a local explanation that is looked for when the prediction achieved by an ML model on an input instance x differs from what was foreseen. A contrastive explanation indicates how to change x to another instance x_c from which a prediction that complies with the user’s expectations can be obtained. In this paper, we present a constraint-based approach to the generation of contrastive explanations that are suited to regression functions represented by boosted trees. We show how to compute the smallest interval containing all the regression values that are attainable given a set of characteristics of x that are protected (i.e., not amenable to change). We also show how to generate minimal contrastive explanations for x given a target interval, i.e., instances with regression values within the specified interval and that are as close as possible to x. Closeness is captured using user-dependent mappings reflecting preferences about value change for the attributes (or combinations of attributes) considered in the representation of x.