@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
    '\n        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",                  quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99],                  quantilesCol=None, aggregationDepth=2)\n        '
    super(AFTSurvivalRegression, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.AFTSurvivalRegression', self.uid)
    self._setDefault(censorCol='censor', quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], maxIter=100, tol=1e-06)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)
__init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",                  quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99],                  quantilesCol=None, aggregationDepth=2)
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
    '\n        \n        '
    super(AFTSurvivalRegression, self).__init__()
    self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.AFTSurvivalRegression', self.uid)
    self._setDefault(censorCol='censor', quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], maxIter=100, tol=1e-06)
    kwargs = self._input_kwargs
    self.setParams(**kwargs)