Mixture of Decision Trees for Interpretable Machine Learning

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a single decision tree, but which can alternatively be divided into subproblems. Each subproblem can then be learned well from a single decision tree. Therefore, MoDT can be considered as a method that improves performance while maintaining interpretability by making each of its decisions understandable and traceable to humans.Our work is accompanied by a Python implementation, which uses an interpretable gating function, a fast learning algorithm, and a direct interface to fine-tuned interpretable visualization methods. The experiments confirm that the implementation works and, more importantly, show the superiority of our approach compared to single decision trees and random forests of similar complexity.

Details

Original languageEnglish
Title of host publicationProceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022
EditorsM. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan
Pages1175-1182
Number of pages8
ISBN (electronic)9781665462839
Publication statusPublished - Dec 2022
Peer-reviewedYes

External IDs

Scopus 85152213983
Mendeley 617748ce-47fd-301b-83e6-9c9156ea1904

Keywords

Keywords

  • decision trees, ensemble, explainability, interpretability, machine learnin, mixture of experts