@keyword_only
@since('1.4.0')
def setParams(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, impuriy='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol='', minWeightFractionPerNode=0.0, weightCol=None):
    '\n        setParams(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        Sets params for Gradient Boosted Tree Regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)
setParams(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)
Sets params for Gradient Boosted Tree Regression.
@keyword_only
@since('1.4.0')
def setParams(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, impuriy='variance', featureSubsetStrategy='all', validationTol=0.01, validationIndicatorCol=None, leafCol=, minWeightFractionPerNode=0.0, weightCol=None):
    '\n        setParams(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        Sets params for Gradient Boosted Tree Regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)