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: Standardize columns before computing distances
  
  Type: ``bool``, defaults to ``True``.
  
  :examples:
  
  >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
  >>> predictors = boston.columns[:-1]
  >>> boston['chas'] = boston['chas'].asfactor()
  >>> train, valid = boston.split_frame(ratios=[.8])
  >>> boston_km = H2OKMeansEstimator(standardize=True)
  >>> boston_km.train(x=predictors,
  ...                 training_frame=train,
  ...                 validation_frame=valid)
  >>> boston_km.scoring_history()
docstr_tok: ["Standardize","columns","before","computing","distances","Type",":","``","bool","``",",","defaults","to","``","True","``",".",":","examples",":",">",">",">","boston","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/gbm_test\/BostonHousing.csv","''",")",">",">",">","predictors","=","boston.columns","[",":","-1","]",">",">",">","boston","[","'chas","'","]","=","boston","[","'chas","'","]",".asfactor","(",")",">",">",">","train",",","valid","=","boston.split_frame","(","ratios=","[",".8","]",")",">",">",">","boston_km","=","H2OKMeansEstimator","(","standardize=True",")",">",">",">","boston_km.train","(","x=predictors",",","...","training_frame=train",",","...","validation_frame=valid",")",">",">",">","boston_km.scoring_history","(",")"]
code: def standardize(self):
      """
      Standardize columns before computing distances
  
      Type: ``bool``, defaults to ``True``.
  
      :examples:
  
      >>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
      >>> predictors = boston.columns[:-1]
      >>> boston['chas'] = boston['chas'].asfactor()
      >>> train, valid = boston.split_frame(ratios=[.8])
      >>> boston_km = H2OKMeansEstimator(standardize=True)
      >>> boston_km.train(x=predictors,
      ...                 training_frame=train,
      ...                 validation_frame=valid)
      >>> boston_km.scoring_history()
      """
      return self._parms.get("standardize")
  
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 32 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.standardize() and returns: self