Github user chouqin commented on the pull request:
https://github.com/apache/spark/pull/2780#issuecomment-59007084
@manishamde thanks for your comments. I will adjust my code after #2785
gets merged.
As for performance, yes, this is slower than the current implementation,
but I think this performance loss is lot very important, in that:
1. It is a O(n) operation, where n is the size of `featureSamples`,
compared to the sort operation(O(nlog(n))),
2. This function is called only once for each continuous feature, and It is
done before the training step.
It should not be a bottleneck because we can view it as a pre-process step.
I am not very sure how much gain in accuracy this change will
give(@jkbradley, are there any references to do this change). If it gives
sufficient accuracy gain,
then this performance loss can be ignored.
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