ROC Curve
As we already introduced Precision and Recall the ROC curve is another way of looking at the quality of classification algorithms.
ROC stands for Receiver Operating Characteristic
The ROC curve is created by plotting the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at various threshold settings.
You already know the TPR as recall or sensitivity.
The false positive rate is defined as FPR = FP / (FP + TN)
Advantage
ROC curves have a big advantage: they are insensitive to changes in class distribution.
sklearn
from sklearn.metrics import roc_curve
