mlsanityData to 7 Oct 2026

Born-Again Tree Ensembles

arXiv 2003.11132 ↗First posted 24 Mar 2020cs.LG stat.MLcited by 38

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Abstract (arXiv)

The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2020.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 7; 2024: 6; 2025: 4; 2026: 5.

38 papers.

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  1. 2021Fast Sparse Decision Tree Optimization via Reference Ensemblescited by 37
  2. 2021Optimal Counterfactual Explanations in Tree Ensemblescited by 36
  3. 2021Provably efficient, succinct, and precise explanationscited by 32
  4. 2021On the price of explainability for some clustering problemscited by 21
  5. 2022Optimal Decision Diagrams for Classificationcited by 10
  6. 2022FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classificationcited by 9
  7. 2022Superpolynomial Lower Bounds for Decision Tree Learning and Testingcited by 9
  8. 2022A Query-Optimal Algorithm for Finding Counterfactualscited by 7
  9. 2023Explainable Data-Driven Optimization: From Context to Decision and Back Againcited by 7
  10. 2024A Theory of Interpretable Approximationscited by 7
  11. 2023Properly Learning Decision Trees with Queries Is NP-Hardcited by 5
  12. 2024Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensemblescited by 4
26 more
  1. 2025Leveraging Predictive Equivalence in Decision Treescited by 4
  2. 2022Explainable Models via Compression of Tree Ensemblescited by 3
  3. 2023Conceptual Views on Tree Ensemble Classifierscited by 3
  4. 2023On Computing Optimal Tree Ensemblescited by 3
  5. 2025From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attackscited by 3
  6. 2022Contextual Decision Treescited by 2
  7. 2023Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built treecited by 2
  8. 2022Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cutscited by 1
  9. 2022Supervised Feature Compression based on Counterfactual Analysiscited by 1
  10. 2022Simplification of Forest Classifiers and Regressorscited by 1
  11. 2024Are Logistic Models Really Interpretable?cited by 1
  12. 2024A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programmingcited by 1
  13. 2024RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifierscited by 1
  14. 2025Cluster-Based Random Forest Visualization and Interpretationcited by 1
  15. 2026Counterfactual Maps: What They Are and How to Find Themcited by 1
  16. 2026Residual-as-Teacher: Mitigating Bias Propagation in Student--Teacher Estimationcited by 1
  17. 2021Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learningcited by 0
  18. 2022TE2Rules: Explaining Tree Ensembles using Rulescited by 0
  19. 2022Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learningcited by 0
  20. 2023Improving the Validity of Decision Trees as Explanationscited by 0
  21. 2023Correlation and Unintended Biases on Univariate and Multivariate Decision Treescited by 0
  22. 2024Minimising changes to audit when updating decision treescited by 0
  23. 2025A Rectification-Based Approach for Distilling Boosted Trees into Decision Treescited by 0
  24. 2026PACE: Prune-And-Compress Ensemble Modelscited by 0
  25. 2026Minimax Rates and Spectral Distillation for Tree Ensemblescited by 0
  26. 2026PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalencecited by 0