From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration

Binary classification tasks are prevalent in learning algorithms, as diverse scenarios require binary decisions. Examples include predicting default risk or accident occurrence in insurance or finance as well as disease likelihood in healthcare. To improve reliability, particularly in sensitive decision-making contexts, a classifier must possess strong discriminatory capabilities. Typically, classifiers are trained to optimize goodness-of-fit criteria, often based on the accuracy of class predictions. However, goodness-of-fit criteria, such as accuracy or AUC, do not consider the varying confidence levels assigned by the algorithm to each prediction. If the sole objective is effective class prediction, then the classifier fulfills its purpose. Nevertheless, there are instances where interest extends beyond the predicted class to the associated likelihood. This occurs when predicting loan repayment defaults (Liu et al., 2021) or accident incidences, as risk transfer pricing is usually tied directly to event probabilities. In such cases, the model-predicted scores of classifiers are often interpreted as event probabilities.