docstr: Plot training set (and validation set if available) scoring history for an H2OBinomialModel.
  
  The timestep and metric arguments are restricted to what is available in its scoring history.
  
  :param str timestep: A unit of measurement for the x-axis.
  :param str metric: A unit of measurement for the y-axis.
  :param bool server: if ``True``, then generate the image inline (using matplotlib's "Agg" backend).
  :param save_plot_path: a path to save the plot via using matplotlib function savefig.
  
  :returns: object that contains the resulting figure (can be accessed using ``result.figure()``)
  
  :examples:
  
  >>> from h2o.estimators import H2OGeneralizedLinearEstimator
  >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
  >>> response = 3
  >>> predictors = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
  >>> model = H2OGeneralizedLinearEstimator(family="binomial")
  >>> model.train(x=predictors, y=response, training_frame=benign)
  >>> model.plot(timestep="AUTO", metric="objective", server=False)
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code: def plot(
      self,
      timestep="AUTO",
      metric="AUTO",
      server=False,
      save_plot_path=None,
  ):
      """
      Plot training set (and validation set if available) scoring history for an H2OBinomialModel.
  
      The timestep and metric arguments are restricted to what is available in its scoring history.
  
      :param str timestep: A unit of measurement for the x-axis.
      :param str metric: A unit of measurement for the y-axis.
      :param bool server: if ``True``, then generate the image inline (using matplotlib's "Agg" backend).
      :param save_plot_path: a path to save the plot via using matplotlib function savefig.
  
      :returns: object that contains the resulting figure (can be accessed using ``result.figure()``)
  
      :examples:
  
      >>> from h2o.estimators import H2OGeneralizedLinearEstimator
      >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
      >>> response = 3
      >>> predictors = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
      >>> model = H2OGeneralizedLinearEstimator(family="binomial")
      >>> model.train(x=predictors, y=response, training_frame=benign)
      >>> model.plot(timestep="AUTO", metric="objective", server=False)
      """
      if not has_extension(self, "ScoringHistory"):
          raise H2OValueError(
              "Scoring history plot is not available for this type of model (%s)."
              % self.algo
          )
  
      valid_metrics = self._allowed_metrics("binomial")
      if valid_metrics is not None:
          assert_is_type(
              metric, "AUTO", *valid_metrics
          ), "metric for H2OBinomialModel must be one of %s" % valid_metrics
      if metric == "AUTO":
          metric = self._default_metric("binomial") or "AUTO"
      return self.scoring_history_plot(
          timestep=timestep,
          metric=metric,
          server=server,
          save_plot_path=save_plot_path,
      )
  
imports: from h2o.utils.compatibility import *
  from h2o.exceptions import H2OValueError
  from h2o.model import ModelBase
  from h2o.model.extensions import has_extension
  from h2o.utils.metaclass import deprecated_params
  from h2o.utils.typechecks import assert_is_type
label_desc: Use image node_id 21 for calling the H2OBinomialModel obj's underlying member method code with example usage: obj.plot(timestep, metric, server, save_plot_path) and returns: self