# Interpreting Models via Single Tree Approximation

arXiv: https://arxiv.org/abs/1610.09036 · HTML: https://mlsanity.com/paper/1610.09036/

## Key facts

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

## Built on by (most cited first, 17 of 17)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [Fast Sparse Decision Tree Optimization via Reference Ensembles](https://mlsanity.com/paper/2112.00798/) (2021)
- [Provably efficient, succinct, and precise explanations](https://mlsanity.com/paper/2111.01576/) (2021)
- [Towards a theory of model distillation](https://mlsanity.com/paper/2403.09053/) (2024)
- [Conclusive Local Interpretation Rules for Random Forests](https://mlsanity.com/paper/2104.06040/) (2021)
- [Superpolynomial Lower Bounds for Decision Tree Learning and Testing](https://mlsanity.com/paper/2210.06375/) (2022)
- [A Query-Optimal Algorithm for Finding Counterfactuals](https://mlsanity.com/paper/2207.07072/) (2022)
- [Properly Learning Decision Trees with Queries Is NP-Hard](https://mlsanity.com/paper/2307.04093/) (2023)
- [Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles](https://mlsanity.com/paper/2404.02942/) (2024)
- [Explainable Models via Compression of Tree Ensembles](https://mlsanity.com/paper/2206.07904/) (2022)
- [On the Relationship Between Interpretability and Explainability in Machine Learning](https://mlsanity.com/paper/2311.11491/) (2023)
- [GLEAMS: Bridging the Gap Between Local and Global Explanations](https://mlsanity.com/paper/2408.05060/) (2024)
- [BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor](https://mlsanity.com/paper/2203.15511/) (2022)
- [PACE: Prune-And-Compress Ensemble Models](https://mlsanity.com/paper/2605.06278/) (2026)
- [Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study](https://mlsanity.com/paper/2112.13285/) (2021)
- [Minimax Rates and Spectral Distillation for Tree Ensembles](https://mlsanity.com/paper/2605.11841/) (2026)

## 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.