Discovering Rule Lists with Preferred Variables

Interpretable machine learning focuses on learning models that are inherently understandable by humans. Even such interpretable models, however, must be trustworthy for domain experts to adopt them. This requires not only accurate predictions, but also reliable explanations that do not contradict a domain expert's knowledge. When considering rule-based models, for example, rules may include certain variables either due to artefacts in the data, or due to the search heuristics used. When such rules are provided as explanations, this may lead to distrust. We investigate whether human guidance could benefit interpretable machine learning when it comes to learning models that provide both accurate predictions and reliable explanations. The form of knowledge that we consider is that of preferred variables, i.e., variables that the domain expert deems important enough to be given higher priority than the other variables. We study this question for the task of multiclass classification, use probabilistic rule lists as interpretable models, and use the minimum description length (MDL) principle for model selection. We propose S-Classy, an algorithm based on beam search that learns rule lists and takes preferred variables into account. We compare S-Classy to its baseline method, i.e., without using preferred variables, and empirically demonstrate that adding preferred variables does not harm predictive performance, while it does result in the preferred variables being used in rules higher up in the learned rule lists.