@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-06, fitIntercept=True, standardization=True, solver='auto', weightCol=None, aggregationDepth=2, loss='squaredError', epsilon=1.35):
    '\n        __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, aggregationDepth=2,                  loss="squaredError", epsilon=1.35)\n        '
    super(LinearRegression, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.LinearRegression', self.uid)
    self._setDefault(maxIter=100, regParam=0.0, tol=1e-06, loss='squaredError', epsilon=1.35)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)
__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, aggregationDepth=2,                  loss="squaredError", epsilon=1.35)
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-06, fitIntercept=True, standardization=True, solver='auto', weightCol=None, aggregationDepth=2, loss='squaredError', epsilon=1.35):
    '\n        \n        '
    super(LinearRegression, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.LinearRegression', self.uid)
    self._setDefault(maxIter=100, regParam=0.0, tol=1e-06, loss='squaredError', epsilon=1.35)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)