@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
    '\n        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01,                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,\n                 weightCol=None)\n        '
    super(GBTRegressor, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GBTRegressor', self.uid)
    self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, leafCol='', minWeightFractionPerNode=0.0)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01,                  validationIndicatorCol=None, leafCol="", minWeightFractionPerNode=0.0,
         weightCol=None)
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, seed=None, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
    '\n        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1, seed=None,                  impurity="variance", featureSubsetStrategy="all", validationTol=0.01,                  validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0,\n                 weightCol=None)\n        '
    super(GBTRegressor, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.GBTRegressor', self.uid)
    self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, checkpointInterval=10, lossType='squared', maxIter=20, stepSize=0.1, impurity='variance', featureSubsetStrategy='all', validationTol=0.01, leafCol=, minWeightFractionPerNode=0.0)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)