# Making Tree Ensembles Interpretable

arXiv: https://arxiv.org/abs/1606.05390 · HTML: https://mlsanity.com/paper/1606.05390/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [Scalable Rule-Based Representation Learning for Interpretable Classification](https://mlsanity.com/paper/2109.15103/) (2021)
- [Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence](https://mlsanity.com/paper/2103.00752/) (2021)
- [Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery](https://mlsanity.com/paper/2108.03013/) (2021)
- [An exact counterfactual-example-based approach to tree-ensemble models interpretability](https://mlsanity.com/paper/2105.14820/) (2021)
- [XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics](https://mlsanity.com/paper/2402.02452/) (2024)
- [A Set Membership Approach to Discovering Feature Relevance and Explaining Neural Classifier Decisions](https://mlsanity.com/paper/2204.02241/) (2022)

## Abstract (arXiv)

Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human. To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the complex ensemble. A synthetic experiment showed that a complicated tree ensemble was approximated reasonably as interpretable.