Consider a binary classification problem.
Intuitively, a value for the area under the curve (for both curves) very close to 1, shows that the curve is almost L-shaped.
Thus, this means that the value on y axis stays rather consistent despite changes in threshold, and if we were to invert the axes, this would hold true for both values plotted.
Does this essentially mean that an L-shaped curve means that the model performs equally well (especially for the PR curve, since precision and recall are used to calculate F1 which is a pretty robust and widely used metric) for all classification thresholds? Or did I make some jump in my logic?