# Extracting Optimal Explanations for Ensemble Trees via Logical Reasoning

arXiv: https://arxiv.org/abs/2103.02191 · HTML: https://mlsanity.com/paper/2103.02191/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2021.

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

- [Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models](https://mlsanity.com/paper/2403.17588/) (2024)
- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)
- [MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest](https://mlsanity.com/paper/2202.12512/) (2022)
- [Reasoning Under Threat: Symbolic and Neural Techniques for Cybersecurity Verification](https://mlsanity.com/paper/2503.22755/) (2025)

## Abstract (arXiv)

Ensemble trees are a popular machine learning model which often yields high prediction performance when analysing structured data. Although individual small decision trees are deemed explainable by nature, an ensemble of large trees is often difficult to understand. In this work, we propose an approach called optimised explanation (OptExplain) that faithfully extracts global explanations of ensemble trees using a combination of logical reasoning, sampling and optimisation. Building on top of this, we propose a method called the profile of equivalent classes (ProClass), which uses MAX-SAT to simplify the explanation even further. Our experimental study on several datasets shows that our approach can provide high-quality explanations to large ensemble trees models, and it betters recent top-performers.