【PLT】ROC

Demo

ROC metric to evaluate classifier output quality using cross-validation (CV)
ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means the top left corner of the plot is the ideal point. A larger area under the curve (AUC) is usually better.

The steepness of ROC curve is also important, since it is ideal to maximize the true positive rate (TPR) while minimizing the false positive rate (FPR).

Code

import numpy as np
import matplotlib.pyplot