RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers

TL;DR

RuleExplorer offers a scalable visualization approach that hierarchically organizes and highlights anomalous rules in tree ensemble classifiers, improving interpretability without losing fidelity.

Contribution

The paper introduces a hierarchical matrix visualization and anomaly-biased rule reduction method for understanding large rule sets in tree ensemble classifiers.

Findings

Enhances interpretability of models with tens of thousands of rules.

Preserves fidelity by adaptively organizing rules hierarchically.

Effectively highlights anomalous rules critical for real-world applications.

Abstract

The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is to address the issue of losing fidelity by adaptively organizing the rules as a hierarchy rather than reducing them. To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level. Synergized with this hierarchical organization of rules, we develop a matrix-based hierarchical visualization to support exploration at different levels of detail. Our quantitative experiments and case studies demonstrate how our method fosters a deeper understanding of both common and anomalous rules, thereby enhancing interpretability without sacrificing comprehensiveness.