# Supersparse Linear Integer Models for Predictive Scoring Systems

arXiv: https://arxiv.org/abs/1306.5860 · HTML: https://mlsanity.com/paper/1306.5860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2013.

## Built on by (most cited first, 3 of 3)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [FasterRisk: Fast and Accurate Interpretable Risk Scores](https://mlsanity.com/paper/2210.05846/) (2022)
- [Robust support vector machines via conic optimization](https://mlsanity.com/paper/2402.01797/) (2024)

## Abstract (arXiv)

We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring systems, and present experimental results to show that SLIM scoring systems are accurate, sparse, and interpretable classification models.