# Computing Abductive Explanations for Boosted Regression Trees

# Computing Abductive Explanations for Boosted Regression Trees

## Gilles Audemard, Steve Bellart, Jean-Marie Lagniez, Pierre Marquis

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Main Track. Pages 3432-3441.
https://doi.org/10.24963/ijcai.2023/382

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.

Keywords:

Machine Learning: ML: Explainable/Interpretable machine learning

Constraint Satisfaction and Optimization: CSO: Constraint programming

Machine Learning: ML: Regression