Github user bgreeven commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-67915148
@jkbradley @avulanov
Agree that we should refrain from adding to much options at this point in
time, and keep the implementation simple but robust.
Concerning interchangeable optimisers: I am developing a preference for
using the case classes as discussed before. This will also get rid of the
plurality of training functions, since the case class instance includes the
default parameters or changed parameters if set by the application. No matter
default or customised values, the case class instance can be input to a single
train function.
When to do this is the question though, especially since such solution
could be useful for other learning algorithms as well. However, if we don't do
it now, we will have to accept that we will have to keep the different training
functions for backward compatibility reasons for at least some time in the
future.
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