For binary classifiers, calibration measures how classifier scores compare to the proportion of positive examples. If the classifier is well-calibrated, the classifier score is approximately equal to the proportion of positive examples. This is important if the scores are used as probabilities for making decisions via expected cost. Otherwise, the calibration curve may still be interesting; the proportion of positive examples should at least be a monotonic function of the score.
I propose that a new method for calibration be added to the class BinaryClassificationMetrics, since calibration seems to fit in with the ROC curve and other classifier assessments.
For more about calibration, see: http://en.wikipedia.org/wiki/Calibration_%28statistics%29#In_classification
Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht. "Binary Classifier Calibration: Non-parametric approach." http://arxiv.org/abs/1401.3390
Alexandru Niculescu-Mizil, Rich Caruana. "Predicting Good Probabilities With Supervised Learning." Appearing in Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 2005. http://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf
"Properties and benefits of calibrated classifiers." Ira Cohen, Moises Goldszmidt. http://www.hpl.hp.com/techreports/2004/HPL-2004-22R1.pdf