Enhancing Conformal Prediction Using E-Test Statistics

arXiv:2403.19082v1 Announce Type: new
Abstract: 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 …

abstract arxiv construction cs.ai cs.lg data framework machine machine learning math.st prediction predictions robust statistics stat.th test type uncertainty