diff options
Diffstat (limited to 'python/pyspark/ml/regression.py')
| -rw-r--r-- | python/pyspark/ml/regression.py | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 56312f672f..19afc723bb 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -39,7 +39,8 @@ __all__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel', @inherit_doc class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept, - HasStandardization, HasSolver, HasWeightCol, JavaMLWritable, JavaMLReadable): + HasStandardization, HasSolver, HasWeightCol, HasAggregationDepth, + JavaMLWritable, JavaMLReadable): """ Linear regression. @@ -97,11 +98,11 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, - standardization=True, solver="auto", weightCol=None): + standardization=True, solver="auto", weightCol=None, aggregationDepth=2): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ - standardization=True, solver="auto", weightCol=None) + standardization=True, solver="auto", weightCol=None, aggregationDepth=2) """ super(LinearRegression, self).__init__() self._java_obj = self._new_java_obj( @@ -114,11 +115,11 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction @since("1.4.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, - standardization=True, solver="auto", weightCol=None): + standardization=True, solver="auto", weightCol=None, aggregationDepth=2): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ - standardization=True, solver="auto", weightCol=None) + standardization=True, solver="auto", weightCol=None, aggregationDepth=2) Sets params for linear regression. """ kwargs = self.setParams._input_kwargs |