Calibrating sufficiently

Binary classification, in the first place, deals with decision tools (classifiers) that facilitate the prediction of the classes of instances on the basis of the so-called features of the instances. Accordingly, the simplest classifiers are crisp (or discrete) in the sense of having the set {0, 1} as output range: 1 for'predict positive class', 0 for'predict negative class. Scoring (or soft) classifiers provide output in a continuous range, usually with the interpretation that high values indicate high likelihood of the instance belonging to the positive class, while low values suggest that membership of the negative class is more likely. In many applications of classification, there is a need for'calibrated' probabilistic classifiers which reflect the likelihood of the positive class given the features of an instance in a frequentist statistical sense (Platt, 2000; Zadrozny and Elkan, 2002; Cohen and Goldszmidt, 2004; Kull et al., 2017). How to best achieve good calibration and how to measure it are active research areas (Böken, 2021; Roelofs et al., 2020).