An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate.
AUC - ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1. By analogy, the Higher the AUC, the better the model is at distinguishing between patients with the disease and no disease.
AUC stands for "Area under the ROC Curve." That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1).
Summary Indices of Test Performance
TPF=True Positive Fraction (Sensitivity)= TP/ (TP+FN)= a/(a+c)
FNF=False Negative Fraction (1-Sensitivity)= FN/ (TP+FN)= c/(a+c)
TNF=True Negative Fraction (Specificity)= TN/ (TN+FP)= d/(b+d)
FPF=False Positive Fraction (1-specificity)= FP/ (TN+FP)= b/(b+d)
PPV=Positive Predicted Value=TP/(TP+FP)=a/(a+b)
NPV=Negative Predicted Value=TN/(TN+FN)=d/(c+d)