Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19569553
--- 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 --
@codedeft I started with a separate model for boosting but @jkbradley
(quite correctly IMO) convinced me otherwise. :-)
I agree methods like boosting require support such as early stopping,
sequential selection of models, etc. but may be we can handle it as a part of
the model configuration. AdaBoost and RF in some ways are more similar than
AdaBoost and GBT in their combining operation. It might be better to capture
all these nuances in one place. Of course, we can always split them later if we
end up writing a lot of custom logic for each algorithm. Thoughts?
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