# Prediction error quantification through probabilistic scaling -- EXTENDED VERSION

arXiv: https://arxiv.org/abs/2105.14187 · HTML: https://mlsanity.com/paper/2105.14187/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2021.

## Built on by (most cited first, 4 of 4)

- [Bridging conformal prediction and scenario optimization](https://mlsanity.com/paper/2503.23561/) (2025)
- [Rule-based Out-Of-Distribution Detection](https://mlsanity.com/paper/2303.01860/) (2023)
- [Probabilistic Safety Regions Via Finite Families of Scalable Classifiers](https://mlsanity.com/paper/2309.04627/) (2023)
- [CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence](https://mlsanity.com/paper/2309.01778/) (2023)

## Abstract (arXiv)

In this paper, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sample-based approach, a probabilistic upper bound on the absolute value of the prediction error. The proposed scheme is based on a probabilistic scaling methodology in which the number of required randomized samples is independent of the complexity of the prediction model. The methodology is extended to address the case in which the probabilistic uncertain quantification is required to be valid for every member of a finite family of predictors. We illustrate the results of the paper by means of a numerical example.