Explainable Models via Compression of Tree Ensembles

TL;DR

This paper introduces CoTE, a method to compress large ensemble models of relational decision trees into a single, interpretable decision list, maintaining effectiveness while enhancing interpretability.

Contribution

The paper presents CoTE, a novel approach for compressing ensemble models into a single decision list to improve interpretability without significant loss of performance.

Findings

CoTE effectively compresses ensemble models into small decision lists.

Experimental results show CoTE maintains high accuracy on benchmark datasets.

CoTE enhances interpretability of complex ensemble models.

Abstract

Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of compressing a large set of learned trees into a single explainable model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that produces a single small decision list as a compressed representation. CoTE first converts the trees to decision lists and then performs the combination and compression with the aid of the original training set. An experimental evaluation demonstrates the effectiveness of CoTE in several benchmark relational data sets.