docstr: Get the between cluster sum of squares.
  
  If all are ``False`` (default), then return the training metric value.
  If more than one option is set to ``True``, then return a dictionary of metrics where
  the keys are "train", "valid", and "xval".
  
  :param bool train: If ``True``, return the between cluster sum of squares value for the training data.
  :param bool valid: If ``True``, return the between cluster sum of squares value for the validation data.
  :param bool xval: If ``True``, return the between cluster sum of squares value for each of the
      cross-validated splits.
  
  :returns: The between cluster sum of squares values for the specified key(s).
  
  :examples:
  
  >>> from h2o.estimators.kmeans import H2OKMeansEstimator
  >>>
  >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
  >>> km = H2OKMeansEstimator(k=3, nfolds=3)
  >>> km.train(x=list(range(4)), training_frame=iris)
  >>> betweenss = km.betweenss() # <- Default: return training metrics
  >>> betweenss
  >>> betweenss3 = km.betweenss(train=False,
  ...                           valid=False,
  ...                           xval=True)
  >>> betweenss3
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code: def betweenss(self, train=False, valid=False, xval=False):
      """
      Get the between cluster sum of squares.
  
      If all are ``False`` (default), then return the training metric value.
      If more than one option is set to ``True``, then return a dictionary of metrics where
      the keys are "train", "valid", and "xval".
  
      :param bool train: If ``True``, return the between cluster sum of squares value for the training data.
      :param bool valid: If ``True``, return the between cluster sum of squares value for the validation data.
      :param bool xval: If ``True``, return the between cluster sum of squares value for each of the
          cross-validated splits.
  
      :returns: The between cluster sum of squares values for the specified key(s).
  
      :examples:
  
      >>> from h2o.estimators.kmeans import H2OKMeansEstimator
      >>>
      >>> iris = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
      >>> km = H2OKMeansEstimator(k=3, nfolds=3)
      >>> km.train(x=list(range(4)), training_frame=iris)
      >>> betweenss = km.betweenss() # <- Default: return training metrics
      >>> betweenss
      >>> betweenss3 = km.betweenss(train=False,
      ...                           valid=False,
      ...                           xval=True)
      >>> betweenss3
      """
      tm = ModelBase._get_metrics(self, train, valid, xval)
      m = {}
      for k, v in tm.items():
          m[k] = None if v is None else v._metric_json["betweenss"]
      return list(m.values())[0] if len(m) == 1 else m
  
file_path: h2o-3/h2o-py/h2o/model/models/clustering.py
imports: from h2o.utils.compatibility import *
  from h2o.model import ModelBase
label_desc: Use image node_id 3 for calling the H2OClusteringModel obj's underlying member method code with example usage: obj.betweenss(train, valid, xval) and returns: unknown