Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10607#discussion_r48907182
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
---
@@ -87,6 +87,14 @@ final class DecisionTreeRegressor @Since("1.4.0")
(@Since("1.4.0") override val
trees.head.asInstanceOf[DecisionTreeRegressionModel]
}
+ /** (private[ml]) Train a decision tree on an RDD */
+ private[ml] def train(data: RDD[LabeledPoint],
--- End diff --
GBTs in spark.ml are handled by converting a dataframe to an RDD of
`LabeledPoint` and then working with that during training. I added a new
`train` method to accept an RDD that can be used to train the trees in the GBT
ensemble. I appreciate feedback on this approach or alternative approaches.
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