Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/3320#discussion_r20479768
--- Diff: python/pyspark/mllib/tree.py ---
@@ -181,8 +180,191 @@ def trainRegressor(data, categoricalFeaturesInfo,
>>> model.predict(rdd).collect()
[1.0, 0.0]
"""
- return DecisionTree._train(data, "regression", 0,
categoricalFeaturesInfo,
- impurity, maxDepth, maxBins,
minInstancesPerNode, minInfoGain)
+ return cls._train(data, "regression", 0, categoricalFeaturesInfo,
+ impurity, maxDepth, maxBins,
minInstancesPerNode, minInfoGain)
+
+
+class WeightedEnsembleModel(JavaModelWrapper):
--- End diff --
@mengxr The idea was that ```WeightedEnsembleModel``` model will also
support non-tree based weak learners for boosting. I don't have a strong
preference either way.
May be we could also removed the prefix ```Weighted``` from the
```WeightedEnsembleModel``` to keep the name simple.
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