Computing abductive explanations for boosted regression trees

We present two algorithms for generating (resp. evaluating) abductive explanations for boosted regression trees. Given an instance x and an interval I containing its value F(x) for the boosted regression tree F at hand, the generation algorithm returns a (most general) term t over the Boolean conditions in F such that every instance x ′ satisfying t is such that F(x′) ∈ I . The evaluation algorithm tackles the corresponding inverse problem: given F , x and a term t over the Boolean conditions in F such that t covers x , find the least interval I t such that for every instance x ′ covered by t we have F(x′) ∈ I t . Experiments on various datasets show that the two algorithms are practical enough to be used for generating (resp. evaluating) abductive explanations for boosted regression trees based on a large number of Boolean conditions.