Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/3320#discussion_r20619737
--- 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 --
Yes, if we decide to go down this route of using a new model class per
algo. I will defer this choice to @mengxr and @jkbradley since I am not
well-versed with the new MLlib api to understand the tradeoffs.
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