Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3951#discussion_r23500737
--- Diff: python/pyspark/mllib/tree.py ---
@@ -383,6 +387,129 @@ def trainRegressor(cls, data,
categoricalFeaturesInfo, numTrees, featureSubsetSt
featureSubsetStrategy, impurity, maxDepth,
maxBins, seed)
+class GradientBoostedTreesModel(TreeEnsembleModel):
+ """
+ Represents a gradient-boosted tree model.
+
+ EXPERIMENTAL: This is an experimental API.
+ It will probably be modified in future.
+ """
+
+
+class GradientBoostedTrees(object):
+
+ @classmethod
+ def _train(cls, data, algo, categoricalFeaturesInfo,
+ loss, numIterations, learningRate, maxDepth):
+ first = data.first()
+ assert isinstance(first, LabeledPoint), "the data should be RDD of
LabeledPoint"
+ model = callMLlibFunc("trainGradientBoostedTreesModel", data,
algo, categoricalFeaturesInfo,
+ loss, numIterations, learningRate, maxDepth)
+ return GradientBoostedTreesModel(model)
+
+ @classmethod
+ def trainClassifier(cls, data, categoricalFeaturesInfo,
--- End diff --
Can you please use the same defaults as in the Scala API (here and for
trainRegressor)?
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