mlsanityData to 14 Oct 2026

Interpreting Models via Single Tree Approximation

arXiv 1610.09036 ↗First posted 27 Oct 2016stat.MEcited by 17

Not on the map. Why?

Abstract (arXiv)

We propose a procedure to build a decision tree which approximates the performance of complex machine learning models. This single approximation tree can be used to interpret and simplify the predicting pattern of random forests (RFs) and other models. The use of a tree structure is particularly relevant in medical questionnaires where it enables an adaptive shortening of the questionnaire, reducing response burden. We study the asymptotic behavior of splits and introduce an improved splitting method designed to stabilize tree structure. Empirical studies on both simulation and real data sets illustrate that our method can simultaneously achieve high approximation power and stability.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 3; 2025: 0; 2026: 2.

17 papers.

Sort:

  1. 2021Trustworthy AI: From Principles to Practicescited by 207
  2. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  3. 2021Fast Sparse Decision Tree Optimization via Reference Ensemblescited by 38
  4. 2021Provably efficient, succinct, and precise explanationscited by 32
  5. 2024Towards a theory of model distillationcited by 15
  6. 2021Conclusive Local Interpretation Rules for Random Forestscited 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. 2023Properly Learning Decision Trees with Queries Is NP-Hardcited by 5
  10. 2024Decision Predicate Graphs: Enhancing Interpretability in Tree Ensemblescited by 5
  11. 2022Explainable Models via Compression of Tree Ensemblescited by 3
  12. 2023On the Relationship Between Interpretability and Explainability in Machine Learningcited by 3
5 more
  1. 2024GLEAMS: Bridging the Gap Between Local and Global Explanationscited by 2
  2. 2022BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractorcited by 1
  3. 2026PACE: Prune-And-Compress Ensemble Modelscited by 1
  4. 2021Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Studycited by 0
  5. 2026Minimax Rates and Spectral Distillation for Tree Ensemblescited by 0