ROC (receiver operating characteristic) Curve

A graph of true positive rate versus false positive rate for different classification thresholds in binary classification. The shape of an ROC curve suggests a binary classification model's ability to separate positive classes from negative classes. Suppose, for example, that a binary classification model perfectly separates all the negative classes from all the positive classes: The ROC curve for the preceding model looks as follows: In contrast, the following illustration graphs the raw logistic regression values for a terrible model that can't separate negative classes from positive classes at all: The ROC curve for this model looks as follows: Meanwhile, back in the real world, most binary classification models separate positive and negative classes to some degree, but usually not perfectly. So, a typical ROC curve falls somewhere between the two extremes: The point on an ROC curve closest to (0.0,1.0) theoretically identifies the ideal classification threshold. However, several other real-world issues influence the selection of the ideal classification threshold. For example, perhaps false negatives cause far more pain than false positives. A numerical metric called AUC summarizes the ROC curve into a single floating-point value.

Practitioners refer to roc (receiver operating characteristic) curve when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.