In signal detection theory, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied.
ROC graphs are two-dimensional graphs in which true positive rate is plotted on the Y axis and false positive rate is plotted on the X axis. An ROC graph depicts relative tradeoffs between benefits (true positives) and costs (false positives).
The true positive rate, also known as the hit rate or recall, is calculated as positives correctly identified / total positives. If all positives are correctly identified, we will have a perfect hit rate.
The false positive rate, also known as the false alarm rate, is calculated as negatives incorrectly classified / total negatives. If all negatives were correctly identified, our false alarm rate is zero.
The sensitivity is also known as the true positive rate.
The specificity is calculated as
(True negatives) / (False positives + True negatives)