Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
arXiv:2506.20114v5 Announce Type: replace Abstract: Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator is the flexibility to jointly control the number of rules extracted and the interaction depth of each rule, which improves accuracy. We develop a tailored exact algorithm to efficiently solve optimization problems underlying our estimator and an ap
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Abstract:Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator is the flexibility to jointly control the number of rules extracted and the interaction depth of each rule, which improves accuracy. We develop a tailored exact algorithm to efficiently solve optimization problems underlying our estimator and an approximate algorithm for computing regularization paths, sequences of solutions that correspond to varying model sizes. We also establish novel non-asymptotic prediction error bounds for our proposed approach, comparing it to an oracle that chooses the best data-dependent linear combination of the rules in the ensemble subject to the same complexity constraint as our estimator. The bounds illustrate that the large-sample predictive performance of our estimator is on par with that of the oracle. Through experiments, we demonstrate that our estimator outperforms existing algorithms for rule extraction.
Subjects:
Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:2506.20114 [stat.ML]
(or arXiv:2506.20114v5 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2506.20114
arXiv-issued DOI via DataCite
Submission history
From: Brian Liu [view email] [v1] Wed, 25 Jun 2025 04:06:37 UTC (1,963 KB) [v2] Wed, 2 Jul 2025 14:20:00 UTC (1,963 KB) [v3] Tue, 29 Jul 2025 17:50:49 UTC (1,963 KB) [v4] Thu, 23 Oct 2025 01:28:52 UTC (4,067 KB) [v5] Mon, 30 Mar 2026 23:07:48 UTC (4,067 KB)
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