Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/10607#issuecomment-198033902
Thanks for doing this migration. I checked the PR and it LGTM
Your tests look good to me. The tests all seem fairly close, except for a
couple of outliers, but even those seem within a standard deviation or so (the
2nd value in spark-perf results). Thanks for running them!
Also @MLnick
> As part of those tickets, I think we can clean up this ML impl and
interfaces if required (e.g. we could look at removing theprivate [ml] train
method in favour of one in MLLIb that converts RDDs to DataFrame and calls ML,
we can make more stuff private where possible, etc). But I think it'll be a lot
easier to clean things up once everything is in ML.
If the ML implementation uses RDDs underneath, it will be nice to call
directly into that implementation from spark.mllib in order to avoid
serialization overhead.
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