Github user feynmanliang commented on the pull request:

    https://github.com/apache/spark/pull/7099#issuecomment-117798935
  
    It wasn't clear to me how to use DFs in the result classes I was creating 
since they didn't have access to the model parameters (featuresCol, 
predictionCol, etc). I could add them as constructor params if you think 
that'll be better but it's not clear to me what the benefit of using a DF in 
the result classes is since most use cases will only be interested in the 
summary functions rather than the predictions + labels themselves.


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