# Supersparse Linear Integer Models for Interpretable Classification

arXiv: https://arxiv.org/abs/1306.6677 · HTML: https://mlsanity.com/paper/1306.6677/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2013.

## Built on by (most cited first, 8 of 8)

- [Rethinking Explainability as a Dialogue: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01875/) (2022)
- [Interpretable Clustering via Multi-Polytope Machines](https://mlsanity.com/paper/2112.05653/) (2021)
- [Interpretable and Fair Boolean Rule Sets via Column Generation](https://mlsanity.com/paper/2111.08466/) (2021)
- [Trade-off Between Efficiency and Consistency for Removal-based Explanations](https://mlsanity.com/paper/2210.17426/) (2022)
- [Fair Decision Rules for Binary Classification](https://mlsanity.com/paper/2107.01325/) (2021)
- [Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization](https://mlsanity.com/paper/2502.05286/) (2025)
- [On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods](https://mlsanity.com/paper/2206.13503/) (2022)
- [Additive Large Language Models for Semi-Structured Text](https://mlsanity.com/paper/2511.11922/) (2025)

## Abstract (arXiv)

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.