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: Column with cross-validation fold index assignment per observation.
  
  Type: ``str``.
  
  :examples:
  
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
  >>> fold_numbers.set_names(["fold_numbers"])
  >>> cars = cars.cbind(fold_numbers)
  >>> print(cars['fold_numbers'])
  >>> cars_km = H2OKMeansEstimator(seed=1234)
  >>> cars_km.train(x=predictors,
  ...               training_frame=cars,
  ...               fold_column="fold_numbers")
  >>> cars_km.scoring_history()
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code: def fold_column(self):
      """
      Column with cross-validation fold index assignment per observation.
  
      Type: ``str``.
  
      :examples:
  
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
      >>> fold_numbers.set_names(["fold_numbers"])
      >>> cars = cars.cbind(fold_numbers)
      >>> print(cars['fold_numbers'])
      >>> cars_km = H2OKMeansEstimator(seed=1234)
      >>> cars_km.train(x=predictors,
      ...               training_frame=cars,
      ...               fold_column="fold_numbers")
      >>> cars_km.scoring_history()
      """
      return self._parms.get("fold_column")
  
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 16 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.fold_column() and returns: self