Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/5585#issuecomment-94580875
`ml.tree` and `ml.ensemble` look good. If we want to distinguish decision
tree from tree elements used in hierarchical clustering, we can put them under
separate packages, e.g., `ml.tree` and `ml.clustering.hierachical`. It is not
necessary to create common base classes if the subclasses are not expected to
be called in a generic way.
What do we want to put under `ml.prediction` beside `Predictor`?
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