Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20628796
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -45,146 +43,92 @@ import org.apache.spark.storage.StorageLevel
* but weak hypothesis weights are not computed correctly for LogLoss
or AbsoluteError.
--- End diff --
@manishamde The current explanation is correct for the original Gradient
Boosting algorithm, which uses weak hypothesis weights and is oblivious to the
weak learner being used. Your suggested explanation is really for TreeBoost,
Friedman's improvement to the original algorithm which is specialized for trees
(which we should add at some point but isn't what we're claiming to have now,
I'd say).
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