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: :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.
  :returns: The F0.5 for this set of metrics and thresholds.
  
  :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.F0point5()
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code: def F0point5(self, thresholds=None):
      """
      :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.
      :returns: The F0.5 for this set of metrics and thresholds.
  
      :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.F0point5()
      """
      return self.metric("f0point5", thresholds=thresholds)
  
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 4 for calling the H2OBinomialModelMetrics obj's underlying member method code with example usage: obj.F0point5(thresholds) and returns: self