Plotting an ROC curve without context

How to do it...

An ROC curve is a diagnostic tool for any classifier without any context. No context means that we do not know yet which error type (FP or FN) is less desirable yet. Let us plot it right away using a vector of probabilities, y_pred_proba[:,1]:

from sklearn.metrics import roc_curve

fpr, tpr, ths = roc_curve(y_test, y_pred_proba[:,1])
plt.plot(fpr,tpr)

The ROC is a plot of the FPR (false alarms) in the x axis and TPR (finding everyone with the condition who really has it) in the y axis. Without context, it is a tool to measure classifier performance.

Perfect classifier

A perfect classifier would have a TPR of 1 regardless of the false alarm rate (FAR):

In the preceding graph, FN is very small; so the TPR, TP / (TP + FN), is close to 1. Its ROC curve has an L-shape:

Imperfect classifier

In the following images, the distributions overlap and the categories cannot be distinguished from one another:

In the imperfect classifier, FN and TN are nearly...