Mixture of Decision Trees for Interpretable Machine Learning
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-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 language | English |
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Title of host publication | Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 |
Editors | M. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan |
Pages | 1175-1182 |
Number of pages | 8 |
ISBN (electronic) | 9781665462839 |
Publication status | Published - Dec 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85152213983 |
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Mendeley | 617748ce-47fd-301b-83e6-9c9156ea1904 |
Keywords
ASJC Scopus subject areas
Keywords
- decision trees, ensemble, explainability, interpretability, machine learnin, mixture of experts