Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/9667#issuecomment-169169854
Maybe I misunderstood the need here. Do you want to use the optimizers for
your own custom algorithms? Or do you want to get info back from MLlib
algorithms like LogisticRegression, etc.? I was thinking the latter, which is
supported by some models in spark.ml (like LogisticRegressionModel). However,
if you want the former, there isn't really an optimizer API in spark.ml
yet---but there will be at some point.
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