Enhancing Conformal Prediction Using E-Test Statistics
A. A. Balinsky A. D. Balinsky
Conformal Prediction (CP) serves as a robust framework that quantifies
uncertainty in predictions made by Machine Learning (ML) models. Unlike
traditional point predictors, CP generates statistically valid prediction
regions, also known as prediction intervals, based on the assumption of data
exchangeability. Typically, the construction of conformal predictions hinges on
p-values. This paper, however, ventures down an alternative path, harnessing
the power of e-test statistics to augment the efficacy of conformal predictions
by introducing a BB-predictor (bounded from the below predictor).