[2603.29361] Rigorous Explanations for Tree Ensembles

Computer Science > Artificial Intelligence arXiv:2603.29361 (cs) [Submitted on 31 Mar 2026] Title:Rigorous Explanations for Tree Ensembles Authors:Yacine Izza, Alexey Ignatiev, Xuanxiang Huang, Peter J. Stuckey, Joao Marques-Silva View a PDF of the paper titled Rigorous Explanations for Tree Ensembles, by Yacine Izza and 4 other authors View PDF HTML (experimental) Abstract:Tree ensembles (TEs) find a multitude of practical applications. They represent one of the most general and accurate classes of machine learning methods. While they are typically quite concise in representation, their operation remains inscrutable to human decision makers. One solution to build trust in the operation of TEs is to automatically identify explanations for the predictions made. Evidently, we can only achieve trust using explanations, if those explanations are rigorous, that is truly reflect properties of the underlying predictor they explain This paper investigates the computation of rigorously-defined, logically-sound explanations for the concrete case of two well-known examples of tree ensembles, namely random forests and boosted trees. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO) Cite as: arXiv:2603.29361 [cs.AI]   (or arXiv:2603.29361v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29361 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yacine Izza [view email] [v1...