title: Feature selection and One-Hot-Encoding in Apache Spark
body: <p>I'm working on a classification model and I have a problem to create the correct form of data for the model.
  In my dataset there are 3 columns with sums. I discretized these columns with the given Bucketizer. The rest of the columns are categorical with Strings as values. I used the StringIndexer to transform these features. Afterwards I select the best columns via ChiSqSelector. So far so good.
  But now I want to transform the categorical features in dummy variables. I don't know how to do that because I already have the data in the form of LabeledPoints. Is there a easy way or given solution to transform the values from a set of vectors to dummy variables? Or does anyone has a suggestion to solve this problem in another way?</p>