Post Hoc Calibration

Post-hoc calibration aims to improve the reliability of predictive models, particularly deep neural networks, by adjusting their output probabilities to better reflect true likelihoods without retraining the model itself. Current research focuses on developing and comparing various calibration methods, including isotonic regression, Platt scaling, temperature scaling, and more sophisticated approaches like random forests and cascaded temperature regression, often tailored to specific tasks like object detection or regression. These advancements are significant because well-calibrated models are crucial for trustworthy decision-making in diverse applications, ranging from medical diagnosis to autonomous systems, where accurate uncertainty quantification is paramount.