June 11, 2024, 4:44 a.m. | Masahiro Fujisawa, Futoshi Futami

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.06227v1 Announce Type: cross
Abstract: Nonparametric estimation with binning is widely employed in the calibration error evaluation and the recalibration of machine learning models. Recently, theoretical analyses of the bias induced by this estimation approach have been actively pursued; however, the understanding of the generalization of the calibration error to unknown data remains limited. In addition, although many recalibration algorithms have been proposed, their generalization performance lacks theoretical guarantees. To address this problem, we conduct a generalization analysis of the …

abstract analysis arxiv bayes bias calibration classification cs.lg data error evaluation however machine machine learning machine learning models stat.ml type understanding

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