mlsanityData to 7 Oct 2026

On Explaining Decision Trees

arXiv 2010.11034 ↗First posted 21 Oct 2020cs.LG cs.AIcited by 49

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Abstract (arXiv)

Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI-explanation, i.e. a subset-minimal set of feature values that entails the prediction. As a result, the paper proposes a novel model for computing PI-explanations of DTs, which enables computing one PI-explanation in polynomial time. Moreover, it is shown that enumeration of PI-explanations can be reduced to the enumeration of minimal hitting sets. Experimental results were obtained on a wide range of publicly available datasets with well-known DT-learning tools, and confirm that in most cases DTs have paths that are proper supersets of PI-explanations.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.

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Citing papers per year: 2021: 11; 2022: 15; 2023: 4; 2024: 4; 2025: 9; 2026: 6.

49 papers.

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  1. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  2. 2022Logic-Based Explainability in Machine Learningcited by 47
  3. 2022On Tackling Explanation Redundancy in Decision Treescited by 44
  4. 2022Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networkscited by 42
  5. 2021On the Computational Intelligibility of Boolean Classifierscited by 40
  6. 2021SAT-Based Rigorous Explanations for Decision Listscited by 40
  7. 2021Explanations for Monotonic Classifierscited by 39
  8. 2021On Efficiently Explaining Graph-Based Classifierscited by 37
  9. 2022On Computing Probabilistic Explanations for Decision Treescited by 35
  10. 2021Trading Complexity for Sparsity in Random Forest Explanationscited by 32
  11. 2021Foundations of Symbolic Languages for Model Interpretabilitycited by 26
  12. 2022ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanationscited by 26
37 more
  1. 2021Probabilistic Sufficient Explanationscited by 24
  2. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  3. 2022Computing Abductive Explanations for Boosted Treescited by 23
  4. 2023Logic for Explainable AIcited by 23
  5. 2022On Computing Probabilistic Abductive Explanationscited by 18
  6. 2021Efficient Explanations With Relevant Setscited by 12
  7. 2023Disproving XAI Myths with Formal Methods -- Initial Resultscited by 12
  8. 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasonscited by 11
  9. 2021Model Learning with Personalized Interpretability Estimation (ML-PIE)cited by 9
  10. 2022Interpretability Guarantees with Merlin-Arthur Classifierscited by 8
  11. 2021On the Explanatory Power of Decision Treescited by 6
  12. 2025A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.icited by 6
  13. 2022Efficient Learning of Interpretable Classification Rulescited by 5
  14. 2022On Computing Relevant Features for Explaining NBCscited by 5
  15. 2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
  16. 2021From global to local MDI variable importances for random forests and when they are Shapley valuescited by 4
  17. 2023A Uniform Language to Explain Decision Treescited by 4
  18. 2022Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualizationcited by 3
  19. 2024Logic-based Explanations for Linear Support Vector Classifiers with Reject Optioncited by 3
  20. 2026FAME: Formal Abstract Minimal Explanation for Neural Networkscited by 3
  21. 2024Logic-Based Explainability: Past, Present & Futurecited by 2
  22. 2026Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimizationcited by 2
  23. 2022A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rulescited by 1
  24. 2024Efficient Contrastive Explanations on Demandcited by 1
  25. 2025Feature Relevancy, Necessity and Usefulness: Complexity and Algorithmscited by 1
  26. 2025Extracting Robust Register Automata from Neural Networks over Data Sequencescited by 1
  27. 2022Classifying Human Activities using Machine Learning and Deep Learning Techniquescited by 0
  28. 2022A Model-Agnostic SAT-based Approach for Symbolic Explanation Enumerationcited by 0
  29. 2024Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparencycited by 0
  30. 2025Fixed Point Explainabilitycited by 0
  31. 2025Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision treescited by 0
  32. 2025Next-Depth Lookahead Treecited by 0
  33. 2025Efficient & Correct Predictive Equivalence for Decision Treescited by 0
  34. 2026Formally Explaining Decision Tree Models with Answer Set Programmingcited by 0
  35. 2026On the Hardness of Computing Counterfactual and Semifactual Explanations in XAIcited by 0
  36. 2026Rigorous Explanations for Tree Ensemblescited by 0
  37. 2026ExplAIner: A Declarative Query Language for Explaining Classification Modelscited by 0