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Rigorous Explanations for Tree Ensembles

arXiv 2603.29361 ↗First posted 31 Mar 2026cs.AI cs.LG cs.LOcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 86 references that are in the map. First posted on arXiv 31 Mar 2026.

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References · 27

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15 more
  1. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
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  3. 2020Model Interpretability through the Lens of Computational Complexitycited by 75
  4. 2020Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delaycited by 56
  5. 2020On Explaining Decision Treescited by 49
  6. 2021SAT-Based Rigorous Explanations for Decision Listscited by 40
  7. 2021Explanations for Monotonic Classifierscited by 39
  8. 2021On Efficiently Explaining Graph-Based Classifierscited by 37
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  12. 2022Feature Necessity & Relevancy in ML Classifier Explanationscited by 22
  13. 2023Delivering Inflated Explanationscited by 16
  14. 2025Most General Explanations of Tree Ensembles (Extended Version)cited by 7
  15. 2025Uncovering Bugs in Formal Explainers: A Case Study with PyXAIcited by 1

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