# Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

arXiv: https://arxiv.org/abs/2109.08290 · HTML: https://mlsanity.com/paper/2109.08290/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Interpreting Tree Ensembles with inTrees](https://mlsanity.com/paper/1408.5456/) (2014)
- [Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach](https://mlsanity.com/paper/1606.09066/) (2016)
- [Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME](https://mlsanity.com/paper/1808.00629/) (2018)
- [Using Answer Set Programming for pattern mining](https://mlsanity.com/paper/1409.7777/) (2014)
- [Hybrid ASP-based Approach to Pattern Mining](https://mlsanity.com/paper/1808.07302/) (2018)

## Built on by (most cited first, 4 of 4)

- [FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data](https://mlsanity.com/paper/2110.07843/) (2021)
- [FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data](https://mlsanity.com/paper/2202.06913/) (2022)
- [Reason to explain: Interactive contrastive explanations (REASONX)](https://mlsanity.com/paper/2305.18143/) (2023)
- [Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming](https://mlsanity.com/paper/2410.11000/) (2024)

## Abstract (arXiv)

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract interesting rules. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.