Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20629031
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -40,151 +39,98 @@ import org.apache.spark.storage.StorageLevel
* Notes:
* - This currently can be run with several loss functions. However,
only SquaredError is
* fully supported. Specifically, the loss function should be used to
compute the gradient
- * (to re-label training instances on each iteration) and to weight
weak hypotheses.
+ * (to re-label training instances on each iteration) and to weight
tree ensembles.
* Currently, gradients are computed correctly for the available loss
functions,
- * but weak hypothesis weights are not computed correctly for LogLoss
or AbsoluteError.
- * Running with those losses will likely behave reasonably, but lacks
the same guarantees.
+ * but tree predictions are not computed correctly for LogLoss or
AbsoluteError since they
--- End diff --
Agree.
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