class_docstr: This class is essentially an API for the AUC object.
  This class contains methods for inspecting the AUC for different criteria.
  To input the different criteria, use the static variable ``criteria``.
docstr: Return the coordinates of the ROC curve as a tuple containing the false positive rates as a list and true positive rates as a list.
  :returns: The ROC values.
  
  :examples:
  
  >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
  >>> r = cars[0].runif()
  >>> train = cars[r > .2]
  >>> valid = cars[r <= .2]
  >>> response_col = "economy_20mpg"
  >>> distribution = "bernoulli"
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
  ...                                    distribution=distribution,
  ...                                    fold_assignment="Random")
  >>> gbm.train(x=predictors,
  ...           y=response_col,
  ...           validation_frame=valid,
  ...           training_frame=train)
  >>> gbm.roc(train=True,  valid=False, xval=False)
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code: def roc(self):
      """
      Return the coordinates of the ROC curve as a tuple containing the false positive rates as a list and true positive rates as a list.
      :returns: The ROC values.
  
      :examples:
  
      >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
      >>> r = cars[0].runif()
      >>> train = cars[r > .2]
      >>> valid = cars[r <= .2]
      >>> response_col = "economy_20mpg"
      >>> distribution = "bernoulli"
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
      ...                                    distribution=distribution,
      ...                                    fold_assignment="Random")
      >>> gbm.train(x=predictors,
      ...           y=response_col,
      ...           validation_frame=valid,
      ...           training_frame=train)
      >>> gbm.roc(train=True,  valid=False, xval=False)
      """
      return self.fprs, self.tprs
  
imports: import h2o
  from h2o.model import MetricsBase, ConfusionMatrix
  from h2o.plot import get_matplotlib_pyplot, decorate_plot_result, RAISE_ON_FIGURE_ACCESS
  from h2o.utils.metaclass import deprecated_params
  from h2o.utils.shared_utils import List
  from h2o.utils.typechecks import assert_is_type, numeric, is_type, assert_satisfies
label_desc: Use image node_id 28 for calling the H2OBinomialModelMetrics obj's underlying member method code with example usage: obj.roc() and returns: self,  self