Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/3320#discussion_r20591273 --- 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 -- I started having second thoughts about this too. I vote for having WeightedEnsembleModel be internal, and having it extended by each algorithm's particular model. That will allow the most consistency with the new API.
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