Add probability calibration to the classifier outputs

[sabinthomas]

Classify() needs to implement a threshold mechanism for classify() errors. An error is a condition where the labels and probabilities are inconclusive, and a match cannot be obtained.

One way around this is by computing a priorProbabilities classification, and then comparing every getClassification result to the value of this priorProbabilities

[DrDub]

What you describe seems more in line with application code and it is beyond what a classifier is or does.

But working out confidence levels on the predictions is a direction ML packages are moving towards: http://scikit-learn.org/stable/modules/calibration.html

I'm retitling this and labeling a feature enhancement.