Paper
Measuring Uncertainty Calibration
Authors
Kamil Ciosek, Nicolò Felicioni, Sina Ghiassian, Juan Elenter Litwin, Francesco Tonolini, David Gustaffson, Eva Garcia Martin, Carmen Barcena Gonzales, Raphaëlle Bertrand-Lalo
Abstract
We make two contributions to the problem of estimating the calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error can be upper bounded efficiently without significantly impacting classifier performance and without any restrictive assumptions. All our results are non-asymptotic and distribution-free. We conclude by providing advice on how to measure calibration error in practice. Our methods yield practical procedures that can be run on real-world datasets with modest overhead.