docstr: Get the all thresholds and metric scores in a table.
  
  If all are ``False`` (default), then return the training metric table.
  If more than one option is set to ``True``, then return a dictionary of tables where
  the keys are "train", "valid", and "xval".
  
  :param bool train: If ``True``, return the thresholds and metric scores table for the training data.
  :param bool valid: If ``True``, return the thresholds and metric scores table value for the validation data.
  :param bool xval: If ``True``, return the thresholds and metric scores table value for each of the cross-validated splits.
  
  :returns: The thresholds and metric scores tables for the specified key(s).
  
  :examples:
  
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
  >>> r = cars[0].runif()
  >>> train = cars[r > .2]
  >>> valid = cars[r <=.2] 
  >>> response_col = "economy_20mpg"
  >>> distribution = "bernoulli"
  >>> predictors = ["displacement", "power", "weight", "acceleration", "year"]
  >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
  >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
  ...                                    distribution=distribution,
  ...                                    fold_assignment="Random")
  >>> gbm.train(y=response_col,
  ...           x=predictors,
  ...           validation_frame=valid,
  ...           training_frame=train)
  >>> gbm.thresholds_and_metric_scores()# <- Default: return training metric table
  >>> gbm.thresholds_and_metric_scores(train=True, valid=True, xval=True)
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code: def thresholds_and_metric_scores(
      self, train=False, valid=False, xval=False
  ):
      """
      Get the all thresholds and metric scores in a table.
  
      If all are ``False`` (default), then return the training metric table.
      If more than one option is set to ``True``, then return a dictionary of tables where
      the keys are "train", "valid", and "xval".
  
      :param bool train: If ``True``, return the thresholds and metric scores table for the training data.
      :param bool valid: If ``True``, return the thresholds and metric scores table value for the validation data.
      :param bool xval: If ``True``, return the thresholds and metric scores table value for each of the cross-validated splits.
  
      :returns: The thresholds and metric scores tables for the specified key(s).
  
      :examples:
  
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
      >>> r = cars[0].runif()
      >>> train = cars[r > .2]
      >>> valid = cars[r <=.2]
      >>> response_col = "economy_20mpg"
      >>> distribution = "bernoulli"
      >>> predictors = ["displacement", "power", "weight", "acceleration", "year"]
      >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
      >>> gbm = H2OGradientBoostingEstimator(nfolds=3,
      ...                                    distribution=distribution,
      ...                                    fold_assignment="Random")
      >>> gbm.train(y=response_col,
      ...           x=predictors,
      ...           validation_frame=valid,
      ...           training_frame=train)
      >>> gbm.thresholds_and_metric_scores()# <- Default: return training metric table
      >>> gbm.thresholds_and_metric_scores(train=True, valid=True, xval=True)
      """
      return self._delegate_to_metrics(
          "thresholds_and_metric_scores",
          train=train,
          valid=valid,
          xval=xval,
      )
  
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 1 for calling the H2OBinomialModel obj's underlying member method code with example usage: obj.thresholds_and_metric_scores(train, valid, xval) and returns: self