ML Interview Question: AUC-ROC curve

Anvi_Singh

Explain AUC-ROC curve.

vikram

Receiver Operator Characteristic (ROC) curve is an evaluation metric for binary classification problems. It is a probability curve that plots the TPR (TRUE POSITIVE RATE) against FPR (FALSE POSITIVE RATE) ![]
at various threshold values , whereas AUC stands for AREA UNDER CURVE which represents ability of a binary classifier to distinguish between classes and is used as a summary of the ROC curve.

The higher the AUC, the better the model’s performance at distinguishing between the positive and negative classes.

When AUC = 1, the classifier can correctly distinguish between all the Positive and the Negative class points. If, however, the AUC had been 0, then the classifier would predict all Negatives as Positives and all Positives as Negatives.

When 0.5<AUC<1, there is a high chance that the classifier will be able to distinguish the positive class values from the negative ones. This is so because the classifier is able to detect more numbers of True positives and True negatives than False negatives and False positives.