# A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming

arXiv: https://arxiv.org/abs/2407.00843 · HTML: https://mlsanity.com/paper/2407.00843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 102 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance](https://mlsanity.com/paper/1905.03151/) (2019)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [Born-Again Tree Ensembles](https://mlsanity.com/paper/2003.11132/) (2020)
- [Generalized Linear Rule Models](https://mlsanity.com/paper/1906.01761/) (2019)
- [Interpretable Random Forests via Rule Extraction](https://mlsanity.com/paper/2004.14841/) (2020)
- [Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability](https://mlsanity.com/paper/2206.10610/) (2022)
- [Learning Optimized Or's of And's](https://mlsanity.com/paper/1511.02210/) (2015)
- [Interpretable and Fair Boolean Rule Sets via Column Generation](https://mlsanity.com/paper/2111.08466/) (2021)
- [Interval Temporal Logic Decision Tree Learning](https://mlsanity.com/paper/2003.04952/) (2020)
- [Fairmandering: A column generation heuristic for fairness-optimized political districting](https://mlsanity.com/paper/2103.11469/) (2021)
- [FIRE: An Optimization Approach for Fast Interpretable Rule Extraction](https://mlsanity.com/paper/2306.07432/) (2023)
- [Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree](https://mlsanity.com/paper/2302.07580/) (2023)
- [Knowledge Extraction with Interval Temporal Logic Decision Trees](https://mlsanity.com/paper/2305.16864/) (2023)

## Built on by (most cited first, 1 of 1)

- [Minimax Rates and Spectral Distillation for Tree Ensembles](https://mlsanity.com/paper/2605.11841/) (2026)

## Abstract (arXiv)

Tree ensembles are very popular machine learning models, known for their effectiveness in supervised classification and regression tasks. Their performance derives from aggregating predictions of multiple decision trees, which are renowned for their interpretability properties. However, tree ensemble models do not reliably exhibit interpretable output. Our work aims to extract an optimized list of rules from a trained tree ensemble, providing the user with a condensed, interpretable model that retains most of the predictive power of the full model. Our approach consists of solving a set partitioning problem formulated through Integer Programming. The proposed method works with either tabular or time series data, for both classification and regression tasks, and its flexible formulation can include any arbitrary loss or regularization functions. Our extensive computational experiments offer statistically significant evidence that our method is competitive with other rule extraction methods in terms of predictive performance and fidelity towards the tree ensemble. Moreover, we empirically show that the proposed method effectively extracts interpretable rules from tree ensemble that are designed for time series data.