Github user codedeft commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19563689
--- 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 --
I personally think that Boosted Model can be a separate one from
RandomForestModel. E.g., it's not inconceivable to have boosted models to use
RandomForestModel as its base learners.
And if this were a truly generic weighted ensemble model, then it could
probably live outside of tree.model namespace, since boosting at least in
theory doesn't care whether base learners are trees or not.
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