In the context of machine learning and signal detection theory, the ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
The ROC curve is useful for evaluating the performance of a binary classifier by measuring its ability to correctly classify positive and negative instances. A well-performing classifier will have an ROC curve that is close to the upper left corner of the plot, indicating a high TPR and a low FPR. Conversely, a poorly performing classifier will have an ROC curve that is close to the diagonal line, indicating a low TPR and a high FPR.