[GitHub] spark pull request: [SPARK-4789] [SPARK-4942] [SPARK-5031] [mllib]...

[mengxr <gi...@git.apache.org>]

Github user mengxr commented on the pull request:

    https://github.com/apache/spark/pull/3637#issuecomment-72405143
  
    About the metadata, I'm thinking of creating ML Attribute/VectorAttribute classes that stores feature information, which can be load from/saved to Spark SQL's metadata. It is similar to Weka's Attribute implementation. Since `RDD[LabeledPoint]` doesn't carry this extra information, could we make ML attributes as an input argument to the `train` method? For example
    
    ~~~
    def train(dataset: RDD[LabeledPoint], attributes: (Attribute, VectorAttribute))
    ~~~


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