Conformal prediction offers a method for assessing the reliability of classifier outputs by utilizing calibration data. By analyzing how classifier scores deviate from expected values, one can establish thresholds that help identify true labels. This approach emphasizes the importance of defining a conformity score and understanding the distribution of output scores to enhance prediction accuracy.
