An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve basically plots two parameters: True Positive Rate/Recall – TPR = TP/TP+FN False Positive Rate – FPR = FP/FP+TN An ROC curve plots TPR and FPR at different classification/probability thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both False Positives and alternatively True Positives. In order to compute the points in an ROC curve, one could evaluate a logistic regression model many times with different classification thresholds, but this would be inefficient. Fortunately, there's an efficient, sorting-based algorithm that can provide this information for us, called AUC. AUC stands for "Area under the ROC Curve." i.e. , AUC measures the two-dimensional area underneath the entire ROC curve ranging from (0,0) to (1,1). AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example. AUC ranges in value from 0 to 1. A model whose predictions are 100% incorrect has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.