Distribution-free binary classification: prediction sets, confidence intervals and calibration

18 Jun 2020Chirag GuptaAleksandr PodkopaevAaditya Ramdas

We study three notions of uncertainty quantification---calibration, confidence intervals and prediction sets---for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data. With a focus towards calibration, we establish a 'tripod' of theorems that connect these three notions for score-based classifiers... (read more)

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