mlsanityData to 7 Oct 2026

Interpreting Tree Ensembles with inTrees

arXiv 1408.5456 ↗First posted 23 Aug 2014cs.LG stat.MLcited by 37

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Abstract (arXiv)

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also be formed and used for future prediction. The inTrees framework can applied to both classification and regression problems, and is applicable to many types of tree ensembles, e.g., random forests, regularized random forests, and boosted trees. We implemented the inTrees algorithms in the "inTrees" R package.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Aug 2014.

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Citing papers per year: 2021: 8; 2022: 12; 2023: 3; 2024: 10; 2025: 4.

37 papers.

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  1. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  2. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  3. 2021GLocalX -- From Local to Global Explanations of Black Box AI Modelscited by 48
  4. 2021Fast Sparse Decision Tree Optimization via Reference Ensemblescited by 37
  5. 2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
  6. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  7. 2021Conclusive Local Interpretation Rules for Random Forestscited by 9
  8. 2021AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Boxcited by 8
  9. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  10. 2022Explainable Global Fairness Verification of Tree-Based Classifierscited by 5
  11. 2023Explainable AI applications in the Medical Domain: a systematic reviewcited by 5
  12. 2024Decision Predicate Graphs: Enhancing Interpretability in Tree Ensemblescited by 5
25 more
  1. 2021Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programmingcited by 4
  2. 2022Local Multi-Label Explanations for Random Forestcited by 3
  3. 2025MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretrainingcited by 3
  4. 2022Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learningcited by 2
  5. 2022On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approachcited by 2
  6. 2023A Voting Approach for Explainable Classification with Rule Learningcited by 2
  7. 2024Explaining Predictions by Characteristic Rulescited by 2
  8. 2024A survey and taxonomy of methods interpreting random forest modelscited by 2
  9. 2021Tree-based local explanations of machine learning model predictions, AraucanaXAIcited by 1
  10. 2022Principled Diverse Counterfactuals in Multilinear Modelscited by 1
  11. 2022Explainability in Machine Learning: a Pedagogical Perspectivecited by 1
  12. 2022The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forestcited by 1
  13. 2022An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrivalcited by 1
  14. 2022BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractorcited by 1
  15. 2023Example-Based Explanations of Random Forest Predictionscited by 1
  16. 2024Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest modelscited by 1
  17. 2024Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programmingcited by 1
  18. 2025Surrogate Interpretable Graph for Random Decision Forestscited by 1
  19. 2022TE2Rules: Explaining Tree Ensembles using Rulescited by 0
  20. 2024Combination of Weak Learners eXplanations to Improve Random Forest eXplicability Robustnesscited by 0
  21. 2024An Interpretable Rule Creation Method for Black-Box Models based on Surrogate Trees -- SRulescited by 0
  22. 2024S-SIRUS: an explainability algorithm for spatial regression Random Forestcited by 0
  23. 2024Minimising changes to audit when updating decision treescited by 0
  24. 2025Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forestscited by 0
  25. 2025A Rectification-Based Approach for Distilling Boosted Trees into Decision Treescited by 0