Prediction error quantification through probabilistic scaling -- EXTENDED VERSION

TL;DR

This paper introduces a sample-based probabilistic scaling method to quantify prediction errors, providing bounds that are independent of model complexity and extendable to multiple predictors.

Contribution

It presents a novel probabilistic scaling approach for error quantification that is model-agnostic and applicable to finite predictor families.

Findings

Provides probabilistic upper bounds on prediction errors

Sample size is independent of model complexity

Method extended to multiple predictors

Abstract

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.