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: Retrieve the Gains/Lift table.
  
  :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["cylinders"] = cars["cylinders"].asfactor()
  >>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
  >>> response_col = "cylinders"
  >>> distribution = "multinomial"
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
  ...                                    distribution=distribution)
  >>> gbm.train(x=predictors,
  ...           y = response,
  ...           training_frame = train,
  ...           validation_frame = valid)
  >>> gbm.gains_lift()
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code: def gains_lift(self):
      """Retrieve the Gains/Lift table.
  
      :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["cylinders"] = cars["cylinders"].asfactor()
      >>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
      >>> response_col = "cylinders"
      >>> distribution = "multinomial"
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
      ...                                    distribution=distribution)
      >>> gbm.train(x=predictors,
      ...           y = response,
      ...           training_frame = train,
      ...           validation_frame = valid)
      >>> gbm.gains_lift()
      """
      if "gains_lift_table" in self._metric_json:
          return self._metric_json["gains_lift_table"]
      return None
  
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 32 for calling the H2OBinomialModelMetrics obj's underlying member method code with example usage: obj.gains_lift() and returns: None, self