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

thresholds do not necessarily include the extremes: zero and one. In Scikit-
Learn’s PR curve, the right-most point is clearly different than ours.
"How come the PR curve dips to lower precision? Shouldn’t it always
go up as we raise the threshold, moving to the left along the curve?"
The Precision Quirk
Glad you asked! This is very annoying and somewhat counterintuitive, but it
happens often, so let’s take a closer look at it. To illustrate why this happens, I will
plot the probability lines for three distinct thresholds: 0.4, 0.5, and 0.57.
Figure 3.19 - The precision quirk
At the top, with a threshold of 0.4, we have 15 points on the right (classified as
positive), two of which are false positives. The precision is given by:
But if we move the threshold to the right, up to 0.5, we lose one true positive,
effectively reducing precision:
This is a temporary side effect, though. As we raise the threshold even further to
0.57, we get the benefit of getting rid of a false positive, thus increasing precision:
Classification Threshold | 255