  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.
