Hands-On Data Analysis with Pandas

ROC curve

In addition to using metrics to evaluate classification problems, we can turn to visualizations. By plotting the true positive rate (sensitivity) versus the false positive rate (1 - specificity), we get the Receiver Operating Characteristic (ROC) curve. This curve allows us to visualize the trade-off between the true positive rate and the false positive rate. We can identify a false positive rate that we are willing to accept and use that to find the threshold to use as a cutoff when predicting the class with probabilities using the predict_proba() method in scikit-learn. Say that we find the threshold to be 60%—we would require predict_proba() to return a value greater than or equal to 0.6 to predict the positive class (predict() ...