Computing Improved Explanations for Random Forests: k-Majoritary Reasons

Louenas Bounia, Insaf Setitra

2025

Abstract

This work focuses on improving explanations for random forests, which, although efficient and providing reliable predictions through the combination of multiple decision trees, are less interpretable than individual decision trees. To improve their interpretability, we introduce k-majoritary reasons, which are minimal implicants for inclusion supporting the decisions of at least k trees, where k is greater than or equal to the majority of the trees in the forest. These reasons are robust and provide a better explanation of the forest’s decision. However, due to their large size and our cognitive limitations, they may be too hard to interpret. To overcome this obstacle, we propose probabilistic majoritary explanations, which provide a more concise interpretation while maintaining a strict majority of trees. We identify the computational complexity of these explanations and propose algorithms to generate them. Our experiments demonstrate the effectiveness of these algorithms and the improvement in interpretability in terms of size provided by probabilistic majoritary explanations (δprobable majoritary reasons).

Paper Citation

in Harvard Style

Bounia L. and Setitra I. (2025). Computing Improved Explanations for Random Forests: k-Majoritary Reasons. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 188-198. DOI: 10.5220/0013143100003890

in Bibtex Style

@conference{icaart25,
author={Louenas Bounia and Insaf Setitra},
title={Computing Improved Explanations for Random Forests: k-Majoritary Reasons},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={188-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013143100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Computing Improved Explanations for Random Forests: k-Majoritary Reasons
SN - 978-989-758-737-5
AU - Bounia L.
AU - Setitra I.
PY - 2025
SP - 188
EP - 198
DO - 10.5220/0013143100003890
PB - SciTePress