mlsanityData to 7 Oct 2026

Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

arXiv 1606.09066 ↗First posted 29 Jun 2016stat.MLcited by 27

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Abstract (arXiv)

Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enormous complexity. In this study, we present a method to make a complex tree ensemble interpretable by simplifying the model. Specifically, we formalize the simplification of tree ensembles as a model selection problem. Given a complex tree ensemble, we aim at obtaining the simplest representation that is essentially equivalent to the original one. To this end, we derive a Bayesian model selection algorithm that optimizes the simplified model while maintaining the prediction performance. Our numerical experiments on several datasets showed that complicated tree ensembles were reasonably approximated as interpretable.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2016.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 3; 2024: 10; 2025: 0; 2026: 1.

27 papers.

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  1. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  2. 2021Fast Sparse Decision Tree Optimization via Reference Ensemblescited by 37
  3. 2022Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-makingcited by 14
  4. 2023Learning Interpretable Rules for Scalable Data Representation and Classificationcited by 14
  5. 2021Conclusive Local Interpretation Rules for Random Forestscited by 9
  6. 2021AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Boxcited by 8
  7. 2024A Survey of Explainable Knowledge Tracingcited by 7
  8. 2024A Comprehensive Guide to Explainable AI: From Classical Models to LLMscited by 7
  9. 2023Explainable AI applications in the Medical Domain: a systematic reviewcited by 5
  10. 2024Decision Predicate Graphs: Enhancing Interpretability in Tree Ensemblescited by 5
  11. 2021Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programmingcited by 4
  12. 2021Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problemscited by 3
15 more
  1. 2022Explainable Models via Compression of Tree Ensemblescited by 3
  2. 2022Local Multi-Label Explanations for Random Forestcited by 3
  3. 2024A survey and taxonomy of methods interpreting random forest modelscited by 2
  4. 2022BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractorcited by 1
  5. 2022Simplification of Forest Classifiers and Regressorscited by 1
  6. 2024Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest modelscited by 1
  7. 2024RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifierscited by 1
  8. 2024Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programmingcited by 1
  9. 2022HEX: Human-in-the-loop Explainability via Deep Reinforcement Learningcited by 0
  10. 2022TE2Rules: Explaining Tree Ensembles using Rulescited by 0
  11. 2023Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanationscited by 0
  12. 2024Combination of Weak Learners eXplanations to Improve Random Forest eXplicability Robustnesscited by 0
  13. 2024Minimising changes to audit when updating decision treescited by 0
  14. 2024Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planningcited by 0
  15. 2026Learning High Coverage Discriminative Parsimonious Rulesetscited by 0