Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

curves that intersect each other at some point. If that’s the case, there is no clear
winner.
One possible solution to this dilemma is to look at the area under the curve. The
curve with more area under it wins! Luckily, Scikit-Learn has an auc() (area under
the curve) method, which we can use to compute the area under the curves for our
(good) model:
# Area under the curves of our model
auroc = auc(fpr, tpr)
aupr = auc(rec, prec)
print(auroc, aupr)
Output
0.9797979797979798 0.9854312354312356
Very close to the perfect value of one! But then again, this is a test example—you
shouldn’t expect figures so high in real-life problems. What about the random
model? The theoretical minimum for the area under the worst ROC curve is 0.5,
which is the area under the diagonal. The theoretical minimum for the area under
the worst PR curve is the proportion of positive samples in the dataset, which is
0.55 in our case.
# Area under the curves of the random model
auroc_random = auc(fpr_random, tpr_random)
aupr_random = auc(rec_random, prec_random)
print(auroc_random, aupr_random)
Output
0.505050505050505 0.570559046216941
Close enough; after all, the curves produced by our random model were only
roughly approximating the theoretical ones.
258 | Chapter 3: A Simple Classification Problem