Calibration and discrimination are two independent metrics: discrimination measures the model's ability to rank positives ahead of negatives (such as ROC-AUC), while calibration measures whether the model's output probabilities match true frequencies; commonly used calibration measures include Expected Calibration Error (ECE) and reliability diagrams, and correction methods include Platt scaling, isotonic regression and temperature scaling.