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.

使用模型树作为替代模型，将特征空间通过决策规则划分为可解释的区域，然后利用基于加性主效应的可解释模型来近似黑盒模型的行为，平衡可解释性和性能，对比了四种模型树算法，最后提供了用户特定的推荐概述。