I'm looking at a precision-recall curve for a binary classification task. My precision-recall curve intersects the y-axis (precision) at 60% and the x-axis at 15%. So I get 15% precision at 100% recall.
1) Doesn't this mean that my true label occurs 15% of the time?
2) My curve (using
precision_recall_curve) goes through the point representing 50% precision and 40% recall. But doesn't that imply that there are 20% positive labels? So isn't that an invalid combination of precision and recall values?