Rigorous Explanations for Tree Ensembles

• URL: http://arxiv.org/abs/2603.29361v1
• Date: Tue, 31 Mar 2026 07:34:47 GMT
• Title: Rigorous Explanations for Tree Ensembles
• Abstract summary: Tree ensembles (TEs) find a multitude of practical applications.<n>One solution to build trust in the operation of TEs is to automatically identify explanations for the predictions made.<n>This paper investigates the computation of rigorously-defined, logically-sound explanations for two well-known examples of tree ensembles.
• Score: 25.06971117518282
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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