Classifier calibration

Classifier calibration refers to the adjustment of probability estimates output by a classification model to accurately reflect the true correctness of the predictions. This process is crucial for ensuring the reliability and interpretability of the model in practical applications. The primary goal of classifier calibration is to reduce the discrepancy between predicted probabilities and actual accuracy rates, with common calibration metrics including Expected Calibration Error (ECE) and Maximum Calibration Error (MCE). Effective calibration enables the model to provide more accurate and trustworthy decision support across various application scenarios.