Classifier Calibration

Classifier calibration addresses the crucial issue of aligning a model's predicted probabilities with their empirical frequencies, ensuring reliable uncertainty quantification. Current research focuses on developing and evaluating calibration methods, particularly for multi-class problems and scenarios with data shifts, employing techniques like isotonic regression, energy-based models, and kernel-based approaches. Improved calibration is vital for trustworthy decision-making in high-stakes applications such as healthcare and autonomous driving, enhancing the reliability and interpretability of machine learning models. The field is actively exploring new metrics and calibration strategies to address limitations of existing methods and improve calibration across diverse model architectures and datasets.