docstr: Get the Fallout (AKA False Positive Rate) for a set of thresholds.
  
  If all are ``False`` (default), then return the training metric value.
  If more than one option is set to ``True``, then return a dictionary of metrics where the keys are "train",
  "valid", and "xval".
  
  :param thresholds: thresholds parameter must be a list (e.g. ``[0.01, 0.5, 0.99]``).
      If None, then the threshold maximizing the metric will be used.
  :param bool train: If train is ``True``, then return the fallout value for the training data.
  :param bool valid: If valid is ``True``, then return the fallout value for the validation data.
  :param bool xval:  If xval is ``True``, then return the fallout value for the cross validation data.
  :returns: The fallout for this binomial model.
  
  :examples:
  
  >>> from h2o.grid.grid_search import H2OGridSearch
  >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  >>> training_data = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
  >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
  >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
  ...                                                  hyper_parameters)
  >>> gs.train(x=[3, 4-11],
  ...          y=3,
  ...          training_frame=training_data)
  >>> gs.fallout(train=True)
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code: def fallout(
      self, thresholds=None, train=False, valid=False, xval=False
  ):
      """
      Get the Fallout (AKA False Positive Rate) for a set of thresholds.
  
      If all are ``False`` (default), then return the training metric value.
      If more than one option is set to ``True``, then return a dictionary of metrics where the keys are "train",
      "valid", and "xval".
  
      :param thresholds: thresholds parameter must be a list (e.g. ``[0.01, 0.5, 0.99]``).
          If None, then the threshold maximizing the metric will be used.
      :param bool train: If train is ``True``, then return the fallout value for the training data.
      :param bool valid: If valid is ``True``, then return the fallout value for the validation data.
      :param bool xval:  If xval is ``True``, then return the fallout value for the cross validation data.
      :returns: The fallout for this binomial model.
  
      :examples:
  
      >>> from h2o.grid.grid_search import H2OGridSearch
      >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
      >>> training_data = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'),
      ...                                                  hyper_parameters)
      >>> gs.train(x=[3, 4-11],
      ...          y=3,
      ...          training_frame=training_data)
      >>> gs.fallout(train=True)
      """
      return {
          model.model_id: model.fallout(thresholds, train, valid, xval)
          for model in self.models
      }
  
label_desc: Use image node_id 13 for calling the H2OBinomialGridSearch obj's underlying member method code with example usage: obj.fallout(thresholds, train, valid, xval) and returns: unknown