Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-62623318
@bgreeven @avulanov About supporting various optimizer, updater, gradient,
and error function options, I vote for keeping them as parameters, rather than
having different versions of the class with different names. (The
LogisticRegressionWithX pattern is really awkward and not sustainable if we add
more optimizers.) I vote for separating these into (a) optimizer, (b)
regularization type (translating to updater internally), and (c) error function
(where the gradient should be paired with the error function, not a separate
parameter).
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