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| author | Felix Cheung <felixcheung_m@hotmail.com> | 2016-11-08 16:00:45 -0800 |
|---|---|---|
| committer | Felix Cheung <felixcheung@apache.org> | 2016-11-08 16:00:45 -0800 |
| commit | 55964c15a7b639f920dfe6c104ae4fdcd673705c (patch) | |
| tree | 1e551bd8c155145135acc161f711e0464b053f8c /python/pyspark | |
| parent | 6f7ecb0f2975d24a71e4240cf623f5bd8992bbeb (diff) | |
| download | spark-55964c15a7b639f920dfe6c104ae4fdcd673705c.tar.gz spark-55964c15a7b639f920dfe6c104ae4fdcd673705c.tar.bz2 spark-55964c15a7b639f920dfe6c104ae4fdcd673705c.zip | |
[SPARK-18239][SPARKR] Gradient Boosted Tree for R
## What changes were proposed in this pull request?
Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.
Since this is relatively isolated I'd like to target this for branch-2.1
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes #15746 from felixcheung/rgbt.
Diffstat (limited to 'python/pyspark')
| -rw-r--r-- | python/pyspark/ml/regression.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 9233d2e7e1..0bc319ca4d 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -828,7 +828,7 @@ class DecisionTreeRegressionModel(DecisionTreeModel, JavaMLWritable, JavaMLReada @inherit_doc class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, RandomForestParams, TreeRegressorParams, HasCheckpointInterval, - JavaMLWritable, JavaMLReadable, HasVarianceCol): + JavaMLWritable, JavaMLReadable): """ `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_ learning algorithm for regression. @@ -876,13 +876,13 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, - featureSubsetStrategy="auto", varianceCol=None): + featureSubsetStrategy="auto"): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \ - featureSubsetStrategy="auto", varianceCol=None) + featureSubsetStrategy="auto") """ super(RandomForestRegressor, self).__init__() self._java_obj = self._new_java_obj( @@ -900,13 +900,13 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, - featureSubsetStrategy="auto", varianceCol=None): + featureSubsetStrategy="auto"): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \ impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \ - featureSubsetStrategy="auto", varianceCol=None) + featureSubsetStrategy="auto") Sets params for linear regression. """ kwargs = self.setParams._input_kwargs |