Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/10607#discussion_r48910260
--- 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 --
I like this approach - and it looks to mirror the approach taken when ALS
was ported over (namely their is a train function in the new ALS code marked as
a developer API taking the old format of inputs). We could also convert the RDD
of LabeledPoints to a DataFrame (which is something I remember being asked to
do in one of my previous PRs). @jkbradley & @dbtsai what are your thoughts on
this?
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