label shift
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Minimum-Risk Recalibration of Classifiers
A probabilistic classifier is considered "well calibrated" when its predicted probabilities closely align with the empirical frequencies of the corresponding labels [
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LaSCal: Label-ShiftCalibrationwithouttargetlabels
Calibration error (CE) provides insights into the alignment between the predicted confidence scores and the classifier accuracy.
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AdaptingtoOnlineLabelShiftwith ProvableGuarantees YongBai1, Yu-JieZhang2,1, PengZhao1, MasashiSugiyama3,2, Zhi-HuaZhou1
Thestandard supervised learning paradigm workseffectivelywhentraining data shares the same distribution as the upcoming testing samples.
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