Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3374#discussion_r20629011
--- 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 --
(copying comment here since it was on an outdated diff)
The original explanation is correct for the original Gradient Boosting
algorithm, which uses weak hypothesis weights and is oblivious to the weak
learner being used. This updated 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). So I think the original explanation is more accurate since we do not
claim to implement TreeBoost.
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