In statistics, 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. The curve is created by plotting the
true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as
sensitivity or the
sensitivity index d', known as "d-prime" in signal detection and biomedical informatics, or recall in
machine learning. The false-positive rate is also known as the fall-out and can be calculated as (1 -
specificity). The ROC curve is thus the sensitivity as a function of fall-out. In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the
cumulative distribution function (area under the probability distribution from
to ) of the detection probability in the y-axis versus the cumulative distribution function of the false-alarm probability in x-axis.