Many applications, however, require trustworthy predictions that need to be not only accurate but also well calibrated.
a wellcalibrated model should produce reliable confident estimates for both in-distribution and out-ofdistribution (OOD) data: (1) For in-distribution data, a model should produce predictive probabilities close to the true likelihood for each class, i.e., confidence ≈ true likelihood.