docstr: Retrieve all types of AUUC in a table.
  
  If all are ``False`` (default), then return the training metric AUUC table.
  If more than one option is set to ``True``, then return a dictionary of metrics where the 
  keys are "train" and "valid".
  
  :param bool train: If ``True``, return the AUUC table for the training data.
  :param bool valid: If ``True``, return the AUUC table for the validation data.
   
  :returns: the AUUC table for the specified key(s).
  
  :examples:
   
  >>> from h2o.estimators import H2OUpliftRandomForestEstimator
  >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv")
  >>> treatment_column = "treatment"
  >>> response_column = "conversion"
  >>> train[treatment_column] = train[treatment_column].asfactor()
  >>> train[response_column] = train[response_column].asfactor()
  >>> predictors = ["f1", "f2", "f3", "f4", "f5", "f6"]
  >>>
  >>> uplift_model = H2OUpliftRandomForestEstimator(ntrees=10, 
  ...                                               max_depth=5,
  ...                                               treatment_column=treatment_column,
  ...                                               uplift_metric="kl",
  ...                                               distribution="bernoulli",
  ...                                               min_rows=10,
  ...                                               auuc_type="gain")
  >>> uplift_model.train(y=response_column, x=predictors, training_frame=train)
  >>> uplift_model.auuc_table() # <- Default: return training metric value
  >>> uplift_model.auuc_table(train=True)
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code: def auuc_table(self, train=False, valid=False):
      """
      Retrieve all types of AUUC in a table.
  
      If all are ``False`` (default), then return the training metric AUUC table.
      If more than one option is set to ``True``, then return a dictionary of metrics where the
      keys are "train" and "valid".
  
      :param bool train: If ``True``, return the AUUC table for the training data.
      :param bool valid: If ``True``, return the AUUC table for the validation data.
  
      :returns: the AUUC table for the specified key(s).
  
      :examples:
  
      >>> from h2o.estimators import H2OUpliftRandomForestEstimator
      >>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv")
      >>> treatment_column = "treatment"
      >>> response_column = "conversion"
      >>> train[treatment_column] = train[treatment_column].asfactor()
      >>> train[response_column] = train[response_column].asfactor()
      >>> predictors = ["f1", "f2", "f3", "f4", "f5", "f6"]
      >>>
      >>> uplift_model = H2OUpliftRandomForestEstimator(ntrees=10,
      ...                                               max_depth=5,
      ...                                               treatment_column=treatment_column,
      ...                                               uplift_metric="kl",
      ...                                               distribution="bernoulli",
      ...                                               min_rows=10,
      ...                                               auuc_type="gain")
      >>> uplift_model.train(y=response_column, x=predictors, training_frame=train)
      >>> uplift_model.auuc_table() # <- Default: return training metric value
      >>> uplift_model.auuc_table(train=True)
      """
      return self._delegate_to_metrics(
          method="auuc_table", train=train, valid=valid
      )
  
file_path: h2o-3/h2o-py/h2o/model/models/uplift.py
imports: from h2o.utils.compatibility import *
  from h2o.model import ModelBase
  from h2o.utils.metaclass import deprecated_params_order
label_desc: Use image node_id 8 for calling the H2OBinomialUpliftModel obj's underlying member method code with example usage: obj.auuc_table(train, valid) and returns: self