RAPID: Rapid and Precise Interpretable Decision Sets

Abstract

Interpretable Decision Sets (IDS) is an approach to building transparent and interpretable supervised machine learning models. Unfortunately, IDS does not scale to most commonly encountered big data sets. In this paper, we propose Rapid And Precise Interpretable Decision Sets (RAPID), a faster alternative to IDS. We use the existing formulation of decision set learning and propose a time-efficient learning framework. RAPID has two major improvements over IDS. First, it uses a linear-time randomized Unconstrained Submodular Maximization algorithm to optimize the objective function. Second, we design special data structures, based on Frequent-Pattern (FP) trees to achieve better computational efficiency. In this work, we first perform a time complexity analysis of IDS and RAPID, and show the significant advantages of the proposed method. Next we run our algorithm, along with baselines, on three public datasets. We show comparable accuracy for RAPID, with 10, 000x improvement in running time over IDS. Additionally, due to the significant improvements in running time of RAPID, we can run more extensive hyperparameter search algorithms, leading to comparable accuracy with competitive baseline models.