WIP Some learning interface improvements

* Clarify classification interfaces
Introduce BinaryClassificationModel for all models that derives a binary
classification from a single weight vector. Comprises the tresholding
functionality to derive a prediction from a score. It basically captures
SVMModel and LogisticRegressionModel. The additional trait
ProbabilisticClassificationModel defines probabilities.
ProbabilisticClassificationModel and ClassificationModel are now also
applicable to multi-class problems.

* Distinction between predict
Depending on if a threshold was defined or not, the single function
predict returns a prediction score or a class. This inconsistency is now
resolved by introducing predictScore, predictClass, and
predictProbability, which return the prediction score, the predicted
class and the outcome probability defined by GeneralizedLinearModel,
ClassificationModel, and ProbabilisticClassificationModel, respectively.

* Remove threshold option
By removing the ambiguity of predict by removing the option
threshold. predictScore now returns always the (classification) score
and predictClass always the corresponding class; a threshold is always
necessary for a prediction.