class_docstr: K-means
  
  Performs k-means clustering on an H2O dataset.
class_docstr_tok: ["K-means","Performs","k-means","clustering","on","an","H2O","dataset","."]
docstr: Number of folds for K-fold cross-validation (0 to disable or >= 2).
  
  Type: ``int``, defaults to ``0``.
  
  :examples:
  
  >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
  >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
  ...               "AGP1","AGMN","LIV","AGLP"]
  >>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
  >>> benign_km = H2OKMeansEstimator(nfolds=5, seed=1234)
  >>> benign_km.train(x=predictors,
  ...                 training_frame=train,
  ...                 validation_frame=valid)
  >>> benign_km.scoring_history()
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code: def nfolds(self):
      """
      Number of folds for K-fold cross-validation (0 to disable or >= 2).
  
      Type: ``int``, defaults to ``0``.
  
      :examples:
  
      >>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
      >>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
      ...               "AGP1","AGMN","LIV","AGLP"]
      >>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
      >>> benign_km = H2OKMeansEstimator(nfolds=5, seed=1234)
      >>> benign_km.train(x=predictors,
      ...                 training_frame=train,
      ...                 validation_frame=valid)
      >>> benign_km.scoring_history()
      """
      return self._parms.get("nfolds")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 6 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.nfolds() and returns: self