docstr: Plots training set (and validation set if available) scoring history for an H2ORegressionModel. The ``timestep``
  and ``metric`` arguments are restricted to what is available in its scoring history.
  
  :param timestep: A unit of measurement for the x-axis.
  :param metric: A unit of measurement for the y-axis.
  :param save_plot_path: a path to save the plot via using matplotlib function savefig.
  
  :returns: Object that contains the resulting scoring history plot (can be accessed using ``result.figure()``).
  
  :examples:
  
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> r = cars[0].runif()
  >>> train = cars[r > .2]
  >>> valid = cars[r <= .2]
  >>> response_col = "economy"
  >>> distribution = "gaussian"
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
  ...                                    distribution=distribution,
  ...                                    fold_assignment="Random")
  >>> gbm.train(x=predictors,
  ...           y=response_col,
  ...           training_frame=train,
  ...           validation_frame=valid)
  >>> gbm.plot(timestep="AUTO", metric="AUTO",)
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code: def plot(
      self,
      timestep="AUTO",
      metric="AUTO",
      save_plot_path=None,
      **kwargs,
  ):
      """
      Plots training set (and validation set if available) scoring history for an H2ORegressionModel. The ``timestep``
      and ``metric`` arguments are restricted to what is available in its scoring history.
  
      :param timestep: A unit of measurement for the x-axis.
      :param metric: A unit of measurement for the y-axis.
      :param save_plot_path: a path to save the plot via using matplotlib function savefig.
  
      :returns: Object that contains the resulting scoring history plot (can be accessed using ``result.figure()``).
  
      :examples:
  
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> r = cars[0].runif()
      >>> train = cars[r > .2]
      >>> valid = cars[r <= .2]
      >>> response_col = "economy"
      >>> distribution = "gaussian"
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
      ...                                    distribution=distribution,
      ...                                    fold_assignment="Random")
      >>> gbm.train(x=predictors,
      ...           y=response_col,
      ...           training_frame=train,
      ...           validation_frame=valid)
      >>> gbm.plot(timestep="AUTO", metric="AUTO",)
      """
      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("regression")
      if valid_metrics is not None:
          assert_is_type(
              metric, "AUTO", *valid_metrics
          ), "metric for H2ORegressionModel must be one of %s" % valid_metrics
      if metric == "AUTO":
          metric = self._default_metric("regression") or "AUTO"
      self.scoring_history_plot(
          timestep=timestep,
          metric=metric,
          save_plot_path=save_plot_path,
          **kwargs,
      )
  
file_path: h2o-3/h2o-py/h2o/model/models/regression.py
imports: from h2o.exceptions import H2OValueError
  from h2o.model import ModelBase
  from h2o.model.extensions import has_extension
  from h2o.utils.compatibility import *
  from h2o.utils.shared_utils import _colmean
  from h2o.utils.typechecks import assert_is_type
label_desc: Use image node_id 1 for calling the H2ORegressionModel obj's underlying member method code with example usage: obj.plot(timestep, metric, save_plot_path) without return types