# Conclusive Local Interpretation Rules for Random Forests

arXiv: https://arxiv.org/abs/2104.06040 · HTML: https://mlsanity.com/paper/2104.06040/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 66 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [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)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [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)
- [Interpreting Models via Single Tree Approximation](https://mlsanity.com/paper/1610.09036/) (2016)
- [LionForests: Local Interpretation of Random Forests](https://mlsanity.com/paper/1911.08780/) (2019)

## Built on by (most cited first, 9 of 9)

- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [Local Multi-Label Explanations for Random Forest](https://mlsanity.com/paper/2207.01994/) (2022)
- [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)
- [Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data](https://mlsanity.com/paper/2410.12803/) (2024)
- [BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor](https://mlsanity.com/paper/2203.15511/) (2022)
- [Truthful Meta-Explanations for Local Interpretability of Machine Learning Models](https://mlsanity.com/paper/2212.03513/) (2022)
- [Personalized Interpretable Classification](https://mlsanity.com/paper/2302.02528/) (2023)
- [Local Interpretability of Random Forests for Multi-Target Regression](https://mlsanity.com/paper/2303.16506/) (2023)

## Abstract (arXiv)

In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations for their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people's lives. In the aforementioned sectors, random forest algorithms strive, thus their ability to explain themselves is an obvious requirement. In this paper, we present LionForests, which relies on a preliminary work of ours. LionForests is a random forest-specific interpretation technique, which provides rules as explanations. It is applicable from binary classification tasks to multi-class classification and regression tasks, and it is supported by a stable theoretical background. Experimentation, including sensitivity analysis and comparison with state-of-the-art techniques, is also performed to demonstrate the efficacy of our contribution. Finally, we highlight a unique property of LionForests, called conclusiveness, that provides interpretation validity and distinguishes it from previous techniques.