Github user sethah commented on the issue:
https://github.com/apache/spark/pull/13617
@JeremyNixon Does it make sense to pull the new activation functions out of
this PR and into a standalone? I know this PR depends upon some of them, but
since it's a WIP and the other change is smaller it can likely be merged before
this one.
Regarding the use cases, you mention that MLPR has advantages on
generalizing and learning non-linear relationships (advantages over what is
currently in MLlib, anyway). Linear regression can be used to model non-linear
relationships using some feature engineering, though it can be cumbersome and
is not always practical. MLPR should be better, but presumably takes longer to
train. It might be nice to show example(s) of a case where the output is
non-linear in the features with MLPR and LR in Spark.ML, where LR is used with
polynomial expansion, on a large dataset. Comparing predictive performance and
algorithm runtimes would help paint a clearer picture of the tradeoffs. At some
point, the number of features make modeling higher order interactions with
Linear Regression impractical, but I'm not sure exactly where that point is and
how well MLPR can perform on the same data.
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