摘要：Deep models have advanced prediction in many domains but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large body of work aiming to help humans understand these black box functions to varying levels of granularity – for example through distillation gradients or adversarial examples. These methods however all tackle interpretability as a separate process after training. In this work we take a different approach and explicitly regularize deep models so that they are well-approximated by processes that humans can step through in little time. Specifically we train several families of deep neural networks to resemble compact axis-aligned decision trees without significant compromises in accuracy. The resulting axis-aligned decision functions uniquely make tree regularized models easy for humans to interpret. Moreover for situations in which a single global tree is a poor estimator we introduce a regional tree regularizer that encourages the deep model to resemble a compact axis-aligned decision tree in predefined human-interpretable contexts. Using intuitive toy examples benchmark image datasets and medical tasks for patients in critical care and with HIV we demonstrate that this new family of tree regularizers yield models that are easier for humans to simulate than L1 or L2 penalties without sacrificing predictive power.
