  Surrogate models play a crucial role in retrospectively interpreting complex
and powerful black box machine learning models via model distillation. This
paper focuses on using model-based trees as surrogate models which partition
the feature space into interpretable regions via decision rules. Within each
region, interpretable models based on additive main effects are used to
approximate the behavior of the black box model, striking for an optimal
balance between interpretability and performance. Four model-based tree
algorithms, namely SLIM, GUIDE, MOB, and CTree, are compared regarding their
ability to generate such surrogate models. We investigate fidelity,
interpretability, stability, and the algorithms' capability to capture
interaction effects through appropriate splits. Based on our comprehensive
analyses, we finally provide an overview of user-specific recommendations.
