Methods and Models for Interpretable Linear Classification

Abstract

We present an integer programming framework to build accurate and interpretable discrete linear classification models. Unlike existing approaches, our framework is designed to provide practitioners with the control and flexibility they need to tailor accurate and interpretable models for a domain of choice. To this end, our framework can produce models that are fully optimized for accuracy, by minimizing the 0--1 classification loss, and that address multiple aspects of interpretability, by incorporating a range of discrete constraints and penalty functions. We use our framework to produce models that are difficult to create with existing methods, such as scoring systems and M-of-N rule tables. In addition, we propose specially designed optimization methods to improve the scalability of our framework through decomposition and data reduction. We show that discrete linear classifiers can attain the training accuracy of any other linear classifier, and provide an Occam's Razor type argument as to why the use of small discrete coefficients can provide better generalization. We demonstrate the performance and flexibility of our framework through numerical experiments and a case study in which we construct a highly tailored clinical tool for sleep apnea diagnosis.

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Cited by in corpus (8)

• A Survey on the Explainability of Supervised Machine Learning
• Explaining Classifiers with Causal Concept Effect (CaCE)
• Towards Automatic Concept-based Explanations
• TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning
• Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
• Distilling Interpretable Models into Human-Readable Code
• Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
• Identifying the Most Explainable Classifier