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: Ignore constant columns.
  
  Type: ``bool``, defaults to ``True``.
  
  :examples:
  
  >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
  >>> predictors = ["displacement","power","weight","acceleration","year"]
  >>> cars["const_1"] = 6
  >>> cars["const_2"] = 7
  >>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
  >>> cars_km = H2OKMeansEstimator(ignore_const_cols=True,
  ...                              seed=1234)
  >>> cars_km.train(x=predictors,
  ...               training_frame=train,
  ...               validation_frame=valid)
  >>> cars_km.scoring_history()
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code: def ignore_const_cols(self):
      """
      Ignore constant columns.
  
      Type: ``bool``, defaults to ``True``.
  
      :examples:
  
      >>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
      >>> predictors = ["displacement","power","weight","acceleration","year"]
      >>> cars["const_1"] = 6
      >>> cars["const_2"] = 7
      >>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
      >>> cars_km = H2OKMeansEstimator(ignore_const_cols=True,
      ...                              seed=1234)
      >>> cars_km.train(x=predictors,
      ...               training_frame=train,
      ...               validation_frame=valid)
      >>> cars_km.scoring_history()
      """
      return self._parms.get("ignore_const_cols")
  
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 20 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.ignore_const_cols() and returns: self