Classifier Calibration

Classifier calibration refers to the process of adjusting the output probabilities of a classification model to ensure they align with the true probabilities of correctness. Accurate probability estimation is crucial for enhancing the reliability and decision-making quality of models in applications such as computer vision. Expected Calibration Error (ECE) and Maximum Calibration Error (MCE) are commonly used calibration metrics, and optimizing these metrics can effectively improve the confidence calibration of the model, thereby enhancing its practical value in real-world scenarios.