# Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models

arXiv: https://arxiv.org/abs/2403.17588 · HTML: https://mlsanity.com/paper/2403.17588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 96 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 18 of 18)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [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)
- [Boolean Decision Rules via Column Generation](https://mlsanity.com/paper/1805.09901/) (2018)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [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)
- [Conclusive Local Interpretation Rules for Random Forests](https://mlsanity.com/paper/2104.06040/) (2021)
- [A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment](https://mlsanity.com/paper/1810.07748/) (2018)
- [Programs as Black-Box Explanations](https://mlsanity.com/paper/1611.07579/) (2016)
- [Rule Covering for Interpretation and Boosting](https://mlsanity.com/paper/2007.06379/) (2020)
- [Extracting Optimal Explanations for Ensemble Trees via Logical Reasoning](https://mlsanity.com/paper/2103.02191/) (2021)
- [Visualizing Random Forest with Self-Organising Map](https://mlsanity.com/paper/1405.6684/) (2014)
- [ggRandomForests: Visually Exploring a Random Forest for Regression](https://mlsanity.com/paper/1501.07196/) (2015)
- [Forest Floor Visualizations of Random Forests](https://mlsanity.com/paper/1605.09196/) (2016)

## Built on by (most cited first, 2 of 2)

- [Surrogate Interpretable Graph for Random Decision Forests](https://mlsanity.com/paper/2506.01988/) (2025)
- [RCProb: Probabilistic rule extraction from classification tree ensembles](https://mlsanity.com/paper/2604.25304/) (2026)

## Abstract (arXiv)

Random Forest (RF) is well-known as an efficient ensemble learning method in terms of predictive performance. It is also considered a Black Box because of its hundreds of deep decision trees. This lack of interpretability can be a real drawback for acceptance of RF models in several real-world applications, especially those affecting one's lives, such as in healthcare, security, and law. In this work, we present Forest-ORE, a method that makes RF interpretable via an optimized rule ensemble (ORE) for local and global interpretation. Unlike other rule-based approaches aiming at interpreting the RF model, this method simultaneously considers several parameters that influence the choice of an interpretable rule ensemble. Existing methods often prioritize predictive performance over interpretability coverage and do not provide information about existing overlaps or interactions between rules. Forest-ORE uses a mixed-integer optimization program to build an ORE that considers the trade-off between predictive performance, interpretability coverage, and model size (size of the rule ensemble, rule lengths, and rule overlaps). In addition to providing an ORE competitive in predictive performance with RF, this method enriches the ORE through other rules that afford complementary information. It also enables monitoring of the rule selection process and delivers various metrics that can be used to generate a graphical representation of the final model. This framework is illustrated through an example, and its robustness is assessed through 36 benchmark datasets. A comparative analysis of well-known methods shows that Forest-ORE provides an excellent trade-off between predictive performance, interpretability coverage, and model size.