Methods and Models for Interpretable Linear Classification
We present a new comprehensive approach to create accurate and interpretable linear classication models through mixed-integer programming. Unlike existing approaches to linear classication, our approach can produce models that incorporate a wide range of interpretability-related qualities, and achieve a precise, understandable and pareto-optimal balance between accuracy and interpretability. We demonstrate the value of our approach by using it to train scoring systems, M-of-N rule tables, and \personalized" classication models for applications in medicine, marketing, and crime. In addition, we