Supersparse Linear Integer Models for Interpretable Classification
This provides a practical tool for domains like medicine and criminology where interpretability is crucial, though it is incremental as it builds on existing scoring system concepts.
The paper tackles the problem of creating interpretable classification models by introducing Supersparse Linear Integer Models (SLIM), which optimize for accuracy and sparsity using discrete optimization, and demonstrates their effectiveness in medicine and criminology with competitive performance compared to state-of-the-art models.
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.