Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19564926
--- 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 --
These generalizations will rely on the new ML API (for which there will be
a PR any day now); it makes sense to keep it in the tree namespace since there
is not generic Estimator concept currently. But once we can, I agree it will
be important to generalize meta-algorithms.
With respect to the models, I don't see how the model concepts are
different. The learning algorithms are different, but that will not prevent a
meta-algorithm to use another meta-algorithm as a weak learner (once the new
API is available). (I think it's good to separate the concepts of Estimator
(learning algorithm) and Transformer (learned model) here.) What do you think?
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