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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