A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning

arXiv:2405.02082v1 Announce Type: cross
Abstract: In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with existing models. To what extent the metrics with which such improvements were measured were accurately capturing the intended goal, whether the numerical differences in the resulting values were significant, or whether uncertainty played a role in this study and if it should have been taken …

abstract analysis arxiv comparative study cs.ai cs.lg data data analysis improvements machine machine learning math.st metrics prediction predictive predictive models quantification results stat.ml stat.th study type uncertainty work