Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration

There are several reasons to evaluate a multi-class classifier on other measures than just error rate. Perhaps most importantly, there can be uncertainty about the exact context of classifier deployment, requiring the classifier to perform well with respect to a variety of contexts. This is commonly achieved by creating a scoring classifier which outputs posterior class probability estimates. Proper scoring rules are loss evaluation measures of scoring classifiers which are minimised at the true posterior probabilities. The well-known decomposition of the proper scoring rules into calibration loss and refinement loss has facilitated the development of methods to reduce these losses, thus leading to better classifiers. We propose multiple novel decompositions including one with four terms: adjustment loss, post-adjustment calibration loss, grouping loss and irreducible loss. The separation of adjustment loss from calibration loss requires extra assumptions which we prove to be satisfied for the most frequently used proper scoring rules: Brier score and log-loss. We propose algorithms to perform adjustment as a simpler alternative to calibration.