Mixture of Decision Trees for Interpretable Machine Learning

TL;DR

This paper presents Mixture of Decision Trees (MoDT), an interpretable ensemble method that improves learning performance on complex problems by dividing them into subproblems, each solved by a single decision tree, while maintaining interpretability.

Contribution

The paper introduces MoDT, a novel ensemble approach combining decision trees with a linear gating function, enhancing performance without sacrificing interpretability.

Findings

MoDT outperforms single decision trees and random forests of similar complexity.

The method maintains interpretability through transparent decision traceability.

Experimental results validate the effectiveness and efficiency of MoDT.

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.