Github user codedeft commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19570062
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
---
@@ -26,7 +26,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.{RandomForest, DecisionTree, impurity}
import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}
import org.apache.spark.mllib.tree.configuration.Algo._
-import org.apache.spark.mllib.tree.model.{RandomForestModel,
DecisionTreeModel}
+import org.apache.spark.mllib.tree.model.{WeightedEnsembleModel,
DecisionTreeModel}
--- End diff --
@manishamde Sounds good.
Just a side note. Because RF models tend to be much bigger than boosted
ensembles, we've encountered situations where the model was *too* big to fit in
a single machine memory. RandomForest model is in a way a good model for
embarassingly parallel predictions so a model could potentially reside in a
distributed fashion.
But we haven't yet decided whether we really want to do this (i.e. are
humongous models really useful in practice and do we really expect crazy
scenarios of gigantic models surpassing dozens of GBs?)
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