Class Calibration

Class calibration in machine learning focuses on aligning a model's predicted probabilities with its actual accuracy, ensuring reliable uncertainty quantification. Current research emphasizes developing robust calibration methods, particularly for multi-class problems and scenarios with out-of-distribution data, employing techniques like isotonic regression, kernel methods, and label smoothing adapted for class-wise adjustments. Improved calibration is crucial for building trustworthy AI systems, particularly in high-stakes applications where accurate uncertainty estimates are vital for safe and effective decision-making.