Evaluating model calibration in classification

TL;DR

This paper develops a theoretical framework for evaluating the calibration of probabilistic classifiers, highlighting subtleties in existing methods and introducing new visualization techniques for better understanding miscalibration.

Contribution

It provides a comprehensive theoretical calibration evaluation framework and introduces novel multidimensional reliability diagrams for improved miscalibration analysis.

Findings

Refined interpretation of existing calibration evaluation techniques

Identification of subtleties in calibration assessment

Introduction of multidimensional reliability diagrams

Abstract

Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision making, the main advantage of probabilistic models is their ability to represent uncertainty about predictions. In safety-critical applications, it is pivotal for a model to possess an adequate sense of uncertainty, which for probabilistic classifiers translates into outputting probability distributions that are consistent with the empirical frequencies observed from realized outcomes. A classifier with such a property is called calibrated. In this work, we develop a general theoretical calibration evaluation framework grounded in probability theory, and point out subtleties present in model calibration evaluation that lead to refined interpretations of existing evaluation techniques. Lastly, we propose new ways to quantify and visualize miscalibration in probabilistic classification, including novel multidimensional reliability diagrams.