Github user VinceShieh commented on a diff in the pull request:
https://github.com/apache/spark/pull/14858#discussion_r77138037
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---
@@ -114,10 +115,10 @@ final class QuantileDiscretizer @Since("1.6.0")
(@Since("1.6.0") override val ui
splits(0) = Double.NegativeInfinity
splits(splits.length - 1) = Double.PositiveInfinity
- val distinctSplits = splits.distinct
+ val distinctSplits = splits.filter(!_.isNaN).distinct
--- End diff --
hmm, if we filter NaN before the data, with size m, goes to approxQuantile,
the increased complexity would be o(m), filter NaN in the splits would normally
be more cheap, with number of splits far less than that of entire dataset.
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