Supersparse Linear Integer Models for Interpretable Classification

TL;DR

This paper introduces SLIM, a tool for creating highly accurate, sparse, and interpretable scoring systems for classification, optimized through discrete methods for practical use in fields like medicine and criminology.

Contribution

The paper presents SLIM, a novel discrete optimization approach for generating scoring systems that are both accurate and highly interpretable, with demonstrated practical applications.

Findings

SLIM produces scoring systems that are both accurate and sparse.

SLIM outperforms state-of-the-art models in interpretability and accuracy.

Applications in medicine and criminology show practical utility.

Abstract

Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because they are practical and interpretable. In this paper, we introduce an off-the-shelf tool to create scoring systems that both accurate and interpretable, known as a Supersparse Linear Integer Model (SLIM). SLIM is a discrete optimization problem that minimizes the 0-1 loss to encourage a high level of accuracy, regularizes the L0-norm to encourage a high level of sparsity, and constrains coefficients to a set of interpretable values. We illustrate the practical and interpretable nature of SLIM scoring systems through applications in medicine and criminology, and show that they are are accurate and sparse in comparison to state-of-the-art classification models using numerical experiments.