  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.
