TE2Rules: Explaining Tree Ensembles using Rules

TL;DR

TE2Rules is a novel method that generates rule-based explanations for tree ensemble models, especially effective for minority class explanations, maintaining high fidelity and scalability.

Contribution

Introducing TE2Rules, a new approach that explains tree ensembles with rules, focusing on minority classes and balancing fidelity with runtime efficiency.

Findings

TE2Rules achieves high fidelity explanations.

Scales effectively to large tree ensembles.

Offers a trade-off between runtime and fidelity.

Abstract

Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introduces TE2Rules (Tree Ensemble to Rules), a novel approach for explaining binary classification tree ensemble models through a list of rules, particularly focusing on explaining the minority class. Many state-of-the-art explainers struggle with minority class explanations, making TE2Rules valuable in such cases. The rules generated by TE2Rules closely approximate the original model, ensuring high fidelity, providing an accurate and interpretable means to understand decision-making. Experimental results demonstrate that TE2Rules scales effectively to tree ensembles with hundreds of trees, achieving higher fidelity within runtimes comparable to baselines. TE2Rules allows for a trade-off between runtime and fidelity, enhancing its practical applicability. The implementation is available here: https://github.com/linkedin/TE2Rules.