A while ago, somebody asked about getting a confidence value of a prediction with MLlib's implementation of Naive Bayes's classification. 

I was wondering if there is any plan in the near future for the predict function to return both a label and a confidence/probability? Or could the private variables in the various machine learning models be exposed so we could write our own functions which return both?

Having a confidence/probability could be very useful in real application. For one thing, you can choose to trust the predicted label only if it has a high confidence level. Also, if you want to combine the results from multiple classifiers, the confidence/probability could be used as some kind of weight for combining.