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

• What does the right-most point in the PR curve represent?
• If I raise the threshold, how do I move along the curve?
You should be able to answer all of these questions by referring to the "Metrics"
section. But, if you are eager to get the answers, here they are:
• The threshold of zero corresponds to the right-most point in both curves.
• The threshold of one corresponds to the left-most point in both curves.
• The right-most point in the PR curve represents the proportion of positive
examples in the dataset.
• If I raise the threshold, I am moving to the left along both curves.
Now, let’s double-check our curves with Scikit-Learn’s roc_curve() and
precision_recall_curve() methods:
fpr, tpr, thresholds1 = roc_curve(y_val, probabilities_val)
prec, rec, thresholds2 = \
precision_recall_curve(y_val, probabilities_val)
Figure 3.18 - Scikit-Learn’s curves
Same shapes, different points.
"Why do these curves have different points than ours?"
Simply put, Scikit-Learn uses only meaningful thresholds; that is, those thresholds
that actually make a difference to the metrics. If moving the threshold a bit does
not modify the classification of any points, it doesn’t matter for building a curve.
Also, notice that the two curves have a different number of points because
different metrics have different sets of meaningful thresholds. Moreover, these
254 | Chapter 3: A Simple Classification Problem