# FasterRisk: Fast and Accurate Interpretable Risk Scores

arXiv: https://arxiv.org/abs/2210.05846 · HTML: https://mlsanity.com/paper/2210.05846/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Supersparse Linear Integer Models for Optimized Medical Scoring Systems](https://mlsanity.com/paper/1502.04269/) (2015)
- [Restricted Strong Convexity Implies Weak Submodularity](https://mlsanity.com/paper/1612.00804/) (2016)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Interpretable Classification Models for Recidivism Prediction](https://mlsanity.com/paper/1503.07810/) (2015)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives](https://mlsanity.com/paper/2001.06471/) (2020)
- [Fast Sparse Classification for Generalized Linear and Additive Models](https://mlsanity.com/paper/2202.11389/) (2022)
- [Supersparse Linear Integer Models for Predictive Scoring Systems](https://mlsanity.com/paper/1306.5860/) (2013)

## Built on by (most cited first, 21 of 21)

- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time](https://mlsanity.com/paper/2503.01087/) (2025)
- [On (assessing) the fairness of risk score models](https://mlsanity.com/paper/2302.08851/) (2023)
- [SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration](https://mlsanity.com/paper/2511.03344/) (2025)
- [Fast and Interpretable Mortality Risk Scores for Critical Care Patients](https://mlsanity.com/paper/2311.13015/) (2023)
- [Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect](https://mlsanity.com/paper/2510.12734/) (2025)
- [Feature Importance Measurement based on Decision Tree Sampling](https://mlsanity.com/paper/2307.13333/) (2023)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)
- [Scalable First-order Method for Certifying Optimal k-Sparse GLMs](https://mlsanity.com/paper/2502.09502/) (2025)
- [Leo Breiman, the Rashomon Effect, and the Occam Dilemma](https://mlsanity.com/paper/2507.03884/) (2025)
- [Why are there many equally good models? An Anatomy of the Rashomon Effect](https://mlsanity.com/paper/2601.06730/) (2026)
- [Automatic Construction of Clinical Scoring Systems with LLM Agents](https://mlsanity.com/paper/2601.22324/) (2026)
- [MISS: Multiclass Interpretable Scoring Systems](https://mlsanity.com/paper/2401.05069/) (2024)
- [SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values](https://mlsanity.com/paper/2509.23756/) (2025)
- [The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning](https://mlsanity.com/paper/2511.21799/) (2025)
- [Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series](https://mlsanity.com/paper/2603.02620/) (2026)
- [From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal k-Sparse GLMs](https://mlsanity.com/paper/2605.22188/) (2026)
- [Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment](https://mlsanity.com/paper/2606.02198/) (2026)
- [How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making](https://mlsanity.com/paper/2608.09433/) (2026)

## Abstract (arXiv)

Over the last century, risk scores have been the most popular form of predictive model used in healthcare and criminal justice. Risk scores are sparse linear models with integer coefficients; often these models can be memorized or placed on an index card. Typically, risk scores have been created either without data or by rounding logistic regression coefficients, but these methods do not reliably produce high-quality risk scores. Recent work used mathematical programming, which is computationally slow. We introduce an approach for efficiently producing a collection of high-quality risk scores learned from data. Specifically, our approach produces a pool of almost-optimal sparse continuous solutions, each with a different support set, using a beam-search algorithm. Each of these continuous solutions is transformed into a separate risk score through a "star ray" search, where a range of multipliers are considered before rounding the coefficients sequentially to maintain low logistic loss. Our algorithm returns all of these high-quality risk scores for the user to consider. This method completes within minutes and can be valuable in a broad variety of applications.