Which Spark MLlib regression algorithm is suitable for numeric predictions based on non-numeric features? I am working on Spark MLlib and have a project where I have to make predictions for numeric data based on non-numeric features. I am a bit confused about which regression algorithm to use from Spark MLlib library primarily due to being new at this. The algorithms present in Spark MLlib library are:

-linear models (SVMs, logistic regression, linear regression)
-naive Bayes
-decision trees
-ensembles of trees (Random Forests and Gradient-Boosted Trees)
-isotonic regression 


Can anyone provide me some guidance as to which algorithm will be suitable for predictions for numeric data based on non-numeric features?
I would suggest -ensembles of trees (Random Forests and Gradient-Boosted Trees).

Here is a nice reference for handling such data with decision trees.