That’s a good reference. For the benefit of our readers:
Precision P is defined as the number of true positives TP over the number of true positives plus the number of false positives FP.
P = TP / (TP + FP)
Recall R is defined as the number of true positives TP over the number of true positives plus the number of false negatives FN.
R = TP / (TP+ FN)
These quantities are also related to the F1 score, which is defined as the harmonic mean of precision and recall.
F1 = 2PR / (P+R)
And max(F1) is a good scalar summary for when you want to quantitatively compare multiple entire curves (of different algorithms, for example, or the progress of an algorithm while it is undergoing training).
Side note: @rhyolight, is there a way we can get LaTeX parsing on this forum platform for decent mathematical typesetting?