Learning Locally Interpretable Rule Ensemble
This work addresses the problem of making machine learning models more interpretable for users in domains requiring transparency, though it is incremental as it builds on existing rule ensemble frameworks.
The paper tackles the trade-off between accuracy and interpretability in rule ensemble models by introducing local interpretability, which measures the number of rules needed for individual predictions rather than the whole model. The proposed method uses a regularizer to achieve this, resulting in rule ensembles that explain predictions with fewer rules than existing methods like RuleFit while maintaining similar accuracy.
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.