This paper proposes a new framework for learning a rule ensemble model that is both accurate and interpretable. A rule ensemble is an interpretable model based on the linear combination of weighted rules. In practice, we often face the trade-off between the accuracy and interpretability of rule ensembles. That is, a rule ensemble needs to include a sufficiently large number of weighted rules to maintain its accuracy, which harms its interpretability for human users. To avoid this trade-off and learn an interpretable rule ensemble without degrading accuracy, we introduce a new concept of interpretability, named local interpretability, which is evaluated by the total number of rules necessary to express individual predictions made by the model, rather than to express the model itself. Then, we propose a regularizer that promotes local interpretability and develop an efficient algorithm for learning a rule ensemble with the proposed regularizer by coordinate descent with local search. Experimental results demonstrated that our method learns rule ensembles that can explain individual predictions with fewer rules than the existing methods, including RuleFit, while maintaining comparable accuracy.

翻译：本文提出了一种新的框架，用于学习既准确又可解释的规则集成模型。规则集成是一种基于加权规则线性组合的可解释模型。在实践中，我们常常面临规则集成的准确性与可解释性之间的权衡。也就是说，规则集成需要包含足够多的加权规则以保持其准确性，但这会损害其对人类用户的可解释性。为了避免这种权衡并学习一个不降低准确性的可解释规则集成，我们引入了一个新的可解释性概念——局部可解释性，它通过模型解释单个预测所需规则的总数来评估，而非解释模型本身。然后，我们提出了一种促进局部可解释性的正则化器，并开发了一种高效算法，通过坐标下降与局部搜索来学习带有该正则化器的规则集成。实验结果表明，我们的方法学习的规则集成能够用比现有方法（包括RuleFit）更少的规则解释单个预测，同时保持相当的准确性。