where C is the number of correct predictions, I is the number of incorrect predictions, and U is the number of peptides or amino acids with no predictions . This alternative definition of "precision" aligns with the traditional definition from the binary classification setting, which is the proportion of predictions with scores greater than the specified score threshold that are correct. However, the alternative definition of "recall" is unusual. In the binary classification setting, "recall" is the proportion of examples with positive labels that are correctly predicted to be positive. The alternative definition, on the other hand, is the proportion of the full set of examples that are predicted correctly. As a result, a precision-recall curve that uses the alternative definition differs qualitatively from a traditional precision-recall curve. In particular, when the threshold is moved to the very end of the ranked list, the value of U goes to zero and, hence, the precision and recall are equal. Therefore, a precision-recall curve that employs the alternative definitions above terminates on the line x = y, whereas a traditional precision-recall curve terminates at x = 1, with y being equal to the proportion of positive examples in the dataset (Figure ).