A-PETE: Adaptive Prototype Explanations of Tree Ensembles

TL;DR

A-PETE introduces an automated method for generating prototype explanations for tree ensemble models, using a specialized distance measure and k-medoid clustering to enhance interpretability without sacrificing accuracy.

Contribution

The paper presents A-PETE, a novel algorithm that automates prototype selection for tree ensembles, improving interpretability with competitive accuracy.

Findings

Competitive predictive accuracy compared to previous explanation methods

Provides a sufficient number of prototypes for effective interpretation

Uses a specialized distance measure and modified k-medoid approach

Abstract

The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a modified k-medoid approach. Experiments demonstrated its competitive predictive accuracy with respect to earlier explanation algorithms. It also provides a a sufficient number of prototypes for the purpose of interpreting the random forest classifier.