Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/6300#discussion_r30990339
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
---
@@ -134,13 +135,23 @@ final class RandomForestClassificationModel
private[ml] (
override def treeWeights: Array[Double] = _treeWeights
+ override def transform(dataset: DataFrame): DataFrame = {
+ val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
--- End diff --
You mentioned that we might want to selectively broadcast the model, only
if it's large enough. Do you think that is something we can do here
automatically, or would it need to be a configuration setting?
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