# Identifying a minimal class of models for high-dimensional data

arXiv: https://arxiv.org/abs/1504.00494 · HTML: https://mlsanity.com/paper/1504.00494/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2015.

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

- [Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data](https://mlsanity.com/paper/2308.11446/) (2023)
- [Differentially Private Sampling from Rashomon Sets, and the Universality of Langevin Diffusion for Convex Optimization](https://mlsanity.com/paper/2204.01585/) (2022)
- [Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users](https://mlsanity.com/paper/2505.07100/) (2025)

## Abstract (arXiv)

Model selection consistency in the high-dimensional regression setting can be achieved only if strong assumptions are fulfilled. We therefore suggest to pursue a different goal, which we call a minimal class of models. The minimal class of models includes models that are similar in their prediction accuracy but not necessarily in their elements. We suggest a random search algorithm to reveal candidate models. The algorithm implements simulated annealing while using a score for each predictor that we suggest to derive using a combination of the Lasso and the Elastic Net. The utility of using a minimal class of models is demonstrated in the analysis of two datasets.