What is ROC curve and when to avoid it?

In logistic regression it can output probability of classifying new label as positive(class 1) and if probability is above the threshold point then you predict positive class else negative class. For example if threshold is 20% more labels will be classified as positive leading to increase in recall and decrease in precision. One trick that I started using is changing all the datatypes appropriately to reduce memory, this not only allow your computer to carry larger dataframes however it having right datatypes make computation more efficient. To plot ROC curve we need to measure TPR and FPR at each threshold point. In order to make predictions with different threshold, instead of predicting labels we need to obtain probabilities of new data belonging to each label which can be obtained by predict_proba() .