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 thresholds and metric scores table.
  
  :examples:
  
  >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
  >>> local_data = [[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],
  ...               [1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],
  ...               [0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],
  ...               [0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],[0, 'b']]
  >>> h2o_data = h2o.H2OFrame(local_data)
  >>> h2o_data.set_names(['response', 'predictor'])
  >>> h2o_data["response"] = h2o_data["response"].asfactor()
  >>> gbm = H2OGradientBoostingEstimator(ntrees=1,
  ...                                    distribution="bernoulli")
  >>> gbm.train(x=list(range(1,h2o_data.ncol)),
  ...           y="response",
  ...           training_frame=h2o_data)
  >>> perf = gbm.model_performance()
  >>> perf
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code: def thresholds_and_metric_scores(self):
      """Retrieve the thresholds and metric scores table.
  
      :examples:
  
      >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
      >>> local_data = [[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],
      ...               [1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],[1, 'a'],
      ...               [0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],
      ...               [0, 'b'],[0, 'b'],[0, 'b'],[0, 'b'],[0, 'b']]
      >>> h2o_data = h2o.H2OFrame(local_data)
      >>> h2o_data.set_names(['response', 'predictor'])
      >>> h2o_data["response"] = h2o_data["response"].asfactor()
      >>> gbm = H2OGradientBoostingEstimator(ntrees=1,
      ...                                    distribution="bernoulli")
      >>> gbm.train(x=list(range(1,h2o_data.ncol)),
      ...           y="response",
      ...           training_frame=h2o_data)
      >>> perf = gbm.model_performance()
      >>> perf
      """
      if "thresholds_and_metric_scores" in self._metric_json:
          return self._metric_json["thresholds_and_metric_scores"]
      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 34 for calling the H2OBinomialModelMetrics obj's underlying member method code with example usage: obj.thresholds_and_metric_scores() and returns: None, self