Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/2125#issuecomment-54221304
@manishamde Thank you very much for the review and comments! About to
send a PR with updates which should address everything. One clarification
about the adaptive ordering:
It is actually more accurate to choose a new ordering at every node (and is
required to make this have guarantees and not be a heuristic for regression and
binary classification). It does mean a different set of splits may be
considered at each node, but that split should be tailored specifically for
that node and should give better results.
As far as computation, it does require a sort, but that should be cheap as
long as the number of categories for any feature is not too large. In my
tests, much more (10x - 100x) time is spent on the aggregation than on the
master, so it is not an issue for categorical features with a smallish number
of categories.
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