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A multiclass classifier is said to be top-label calibrated if the reportedprobability for the predicted class -- the top-label -- is calibrated,conditioned on the top-label. This conditioning on the top-label is absent inthe closely related and popular notion of confidence calibration, which weargue makes confidence calibration difficult to interpret for decision-making.We propose top-label calibration as a rectification of confidence calibration.Further, we outline a multiclass-to-binary (M2B) reduction framework thatunifies confidence, top-label, and class-wise calibration, among others. As itsname suggests, M2B works by reducing multiclass calibration to numerous binarycalibration problems, each of which can be solved using simple binarycalibration routines. We instantiate the M2B framework with the well-studiedhistogram binning (HB) binary calibrator, and prove that the overall procedureis multiclass calibrated without making any assumptions on the underlying datadistribution. In an empirical evaluation with four deep net architectures onCIFAR-10 and CIFAR-100, we find that the M2B + HB procedure achieves lowertop-label and class-wise calibration error than other approaches such astemperature scaling. Code for this work is available at\url{https://github.com/aigen/df-posthoc-calibration}.

Top-label calibration and multiclass-to-binary reductions