Post-hoc recalibration methods are widely used to ensure that classifiers provide faithful probability estimates. We argue that parametric recalibration functions based on logistic regression can be m...

Calibration of machine learning classifiers is necessary to obtain reliable and interpretable predictions, bridging the gap between model outputs and actual probabilities. One prominent technique, ...

Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these predictions is generally ...