How can ROC and AUC can help us evaluate our model?

ROC curve can give us a clear idea to set a threshold value to classify the label and also help in model optimization.

A low threshold value we will put most of the predicted observations under the positive category, even when some of them should be placed under the negative category. On the other hand, keeping the threshold at a very high level penalizes the positive category, but the negative category will improve.

For such case an optimum threshold value can give a better accuracy which can be found on ROC curve

ROC curve will look as follows:

On the other hand, Area under curve or AUC curve is utilized for setting the threshold of cut-off

probability to classify the predicted probability into various classes