RAPID: Rapid and Precise Interpretable Decision Sets

By Sunny Dhamnani, Dhruv Singal, Ritwik Sinha, Tharun Mohandoss, M. Dash. 2019

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.

Subfields: Safety, Robustness & Interpretability

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, neutral): “…2016; Yu et al., 2020a; Angelino et al., 2017, 2018; Aı̈vodji et al., 2019) and decision sets (Wang and Rudin, 2015b; Lakkaraju et al., 2016; Ignatiev et al., 2018; Dash et al., 2018b; Malioutov and Meel, 2018; Ghosh and Meel, 2019; Dhamnani et al., 2019; Yu et al., 2020b; Cao et al., 2020).”