  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.
