Revisit the "chance level" for the different displays

@e-pet commented on different PRs & issues some interesting fact. I take the opportunity to consolidate some of those comments here.

First, we use the term "chance" that is ambiguous depending of the displays. The term "baseline" would probably be better. In addition, I checked and I think we should make an extra effort on the definition of the baseline for each of the type of plot: for ROC curve, the baseline is "a random classifier assigning the positive class with probability p and the negative class with probability 1 − p" [1] while for the PR curve, the baseline is derived from the "always-positive classifier" where any recall or precision under π should be discarded [1].

It leads to a second where in the PR curve, we plot the horizontal line derived from the always-positive classifier but we don't discard when recall < π. In this case, as mentioned by @e-pet, it might make sense to show the hyperbolic line of the always-positive classifier instead (cf. Fig. 2 in [1]).

@e-pet feel free to add any other points that you wanted to discuss. Here, I wanted to focus on the one that looks critical and could be addressed.

[1] [Flach, P., & Kull, M. (2015). Precision-recall-gain curves: PR analysis done right. Advances in neural information processing systems, 28.](https://papers.nips.cc/paper_files/paper/2015/file/33e8075e9970de0cfea955afd4644bb2-Paper.pdf)

COMMENT:
Hi @glemaitre, thank you for collecting this here!

I did some further reading in the meantime (and fixed at least one misunderstanding of mine), so let me try to summarize the issue(s).
- My dismissal of the horizontal line derived from the always-positive classifier was wrong. One can construct it this way: have a "model" return completely random scores, and then tune the decision threshold between 0 and 1. This yields exactly that horizontal line. So: the horizontal line is valid (=associated with actual baseline models one can easily construct at every point along the line), and it *is* actually based on a 'chance' baseline. (Might be worth pointing this out somewhere in the documentation, if this is not already the case?)
- Separately from this, Flach and Kull argue that in terms of the area under the PR curve, the baseline to beat is a curve that has $F_1$ score equaling that of the always-positive classifier at every point along the curve. The interpretation is simple: if your model is below this line, you perform worse than a trivial always-positive classifier in terms of $F_1$. This is where the hyperbolic baseline comes from. I am actually not sure what the equivalent curve in the ROC case would be, or whether it differs from the standard diagonal? Note that at least to my current understanding, this baseline is 'virtual' in the sense that it is not trivially possible to actually construct models with the precision / recall values specified by every point along the curve.
- To complicate matters further, there is actually a *third* 'baseline-like' line that one *could* show: this is the unreachable region in PR space, described [here](https://pmc.ncbi.nlm.nih.gov/articles/PMC3858955/) (and also very easy to calculate). Points below this line cannot be reached by any classifier (with important consequences for the validity of naive AUCPR comparisons, as spelled out both in this paper and the Flach/Kull paper).

Labels: Documentation, API