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Surrogate models play a crucial role in retrospectively interpreting complexand powerful black box machine learning models via model distillation. Thispaper focuses on using model-based trees as surrogate models which partitionthe feature space into interpretable regions via decision rules. Within eachregion, interpretable models based on additive main effects are used toapproximate the behavior of the black box model, striking for an optimalbalance between interpretability and performance. Four model-based treealgorithms, namely SLIM, GUIDE, MOB, and CTree, are compared regarding theirability to generate such surrogate models. We investigate fidelity,interpretability, stability, and the algorithms' capability to captureinteraction effects through appropriate splits. Based on our comprehensiveanalyses, we finally provide an overview of user-specific recommendations.

Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation