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: Produce the desired metric plot.
  
  :param type: the type of metric plot. One of (currently supported):
  
      - ROC curve ('roc')
      - Precision Recall curve ('pr')
      - Gains Lift curve ('gainslift')
      
  :param server: if True, generate plot inline using matplotlib's Anti-Grain Geometry (AGG) backend.
  :param save_plot_path: filename to save the plot to.
  :param plot: ``True`` to plot curve; ``False`` to get a tuple of values at axis x and y of the plot 
          (tprs and fprs for AUC, recall and precision for PR).
  
  :returns: None or values of x and y axis of the plot + the resulting plot (can be accessed using ``result.figure()``).
  
  :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()
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> response = "economy_20mpg"
  >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234)
  >>> cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
  >>> cars_gbm.train(x = predictors,
  ...                y = response,
  ...                training_frame = train,
  ...                validation_frame = valid)
  >>> cars_gbm.plot(type="roc")
  >>> cars_gbm.plot(type="pr")
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code: def plot(
      self, type="roc", server=False, save_plot_path=None, plot=True
  ):
      """
      Produce the desired metric plot.
  
      :param type: the type of metric plot. One of (currently supported):
  
          - ROC curve ('roc')
          - Precision Recall curve ('pr')
          - Gains Lift curve ('gainslift')
  
      :param server: if True, generate plot inline using matplotlib's Anti-Grain Geometry (AGG) backend.
      :param save_plot_path: filename to save the plot to.
      :param plot: ``True`` to plot curve; ``False`` to get a tuple of values at axis x and y of the plot
              (tprs and fprs for AUC, recall and precision for PR).
  
      :returns: None or values of x and y axis of the plot + the resulting plot (can be accessed using ``result.figure()``).
  
      :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()
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> response = "economy_20mpg"
      >>> train, valid = cars.split_frame(ratios = [.8], seed = 1234)
      >>> cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
      >>> cars_gbm.train(x = predictors,
      ...                y = response,
      ...                training_frame = train,
      ...                validation_frame = valid)
      >>> cars_gbm.plot(type="roc")
      >>> cars_gbm.plot(type="pr")
  
      """
      assert type in ["roc", "pr", "gains_lift"]
      if type == "roc":
          return self._plot_roc(server, save_plot_path, plot)
      elif type == "pr":
          return self._plot_pr(server, save_plot_path, plot)
      elif type == "gains_lift":
          return self.gains_lift_plot(
              server=server, save_plot_path=save_plot_path, plot=plot
          )
  
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 23 for calling the H2OBinomialModelMetrics obj's underlying member method code with example usage: obj.plot(type, server, save_plot_path, plot) and returns: self, self, self