Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications

The use of proper scoring rules with novel criteria for 1) Hand and Vinciotti’s (2003) localized logistic regression and 2) for interpretable classification trees are illustrated.

Abstract

What are the natural loss functions or fitting criteria for binary class probability estimation? This question has a simple answer: so-called “proper scoring rules”, that is, functions that score probability estimates in view of data in a Fisher-consistent manner. Proper scoring rules comprise most loss functions currently in use: log-loss, squared error loss, boosting loss, and as limiting cases cost-weighted misclassification losses. Proper scoring rules have a rich structure: • Every proper scoring rules is a mixture (limit of sums) of cost-weighted misclassification losses. The mixture is specified by a weight function (or measure) that describes which misclassification cost weights are most emphasized by the proper scoring rule. • Proper scoring rules permit Fisher scoring and Iteratively Reweighted LS algorithms for model fitting. The weights are derived from a link function and the above weight function. • Proper scoring rules are in a 1-1 correspondence with information measures for tree-based classification. • Proper scoring rules are also in a 1-1 correspondence with Bregman distances that can be used to derive general approximation bounds for cost-weighted misclassification errors, as well as generalized bias-variance decompositions. We illustrate the use of proper scoring rules with novel criteria for 1) Hand and Vinciotti’s (2003) localized logistic regression and 2) for interpretable classification trees. We will also discuss connections with exponential loss used in boosting.