A ROC curve is a graph that enables the user to assess the quality of binary classification. A ROC curve displays relation between the share of objects from the total number of attribute bearers that are correctly identifier as attribute bearers, and the share of objects from the total number of objects that are not attribute bearers and that are by error identifier as attribute bearers on varying of decision rule threshold.

Thus, a ROC curve is calculated if an explanatory series is binary.

A ROC curve is plotted by laying off the obtained sensitivity values along the Y axis, and (1 - specificity) along the X axis.