Rigorous Explanations for Tree Ensembles

arXiv cs.AI / 4/1/2026

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Key Points

The paper argues that building trust in tree ensembles requires automatically generated explanations that are rigorously defined and logically sound rather than merely intuitive.
It focuses on computing such rigorous explanations for two widely used tree-ensemble models: random forests and boosted trees.
The work is positioned as addressing the “inscrutability” of tree-ensemble operation despite their typically compact representations.
By targeting explanation rigor, the research aims to ensure that explanations truly reflect properties of the underlying predictor they claim to describe.

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.