WIP : Isotonic calibration

calibration module with platt and Isotonic calibration.

A few issues :

- [x]  nice calibration plots
- [x]  add tests for metrics
- [x]  add narrative doc

I would also add reference to the slightly more recent work on this topic by Alexandru Niculescu-Mizil and Rich Caruana:

http://machinelearning.org/proceedings/icml2005/papers/079_GoodProbabilities_NiculescuMizilCaruana.pdf

In particular they highlight that naive Bayes and max margin models (such as SVMs and boosted trees) are badly uncalibrated by default but in opposite manners.

It would be great to highlight that typical behavior of boosted trees (e.g. using AdaBoostClassifier) in the example and the narrative documentation.