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: Encoding scheme for categorical features
  
  Type: ``Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
  "sort_by_response", "enum_limited"]``, defaults to ``"auto"``.
  
  :examples:
  
  >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
  >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
  >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
  >>> encoding = "one_hot_explicit"
  >>> pros_km = H2OKMeansEstimator(categorical_encoding=encoding,
  ...                              seed=1234)
  >>> pros_km.train(x=predictors,
  ...               training_frame=train,
  ...               validation_frame=valid)
  >>> pros_km.scoring_history()
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code: def categorical_encoding(self):
      """
      Encoding scheme for categorical features
  
      Type: ``Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder",
      "sort_by_response", "enum_limited"]``, defaults to ``"auto"``.
  
      :examples:
  
      >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
      >>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
      >>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
      >>> encoding = "one_hot_explicit"
      >>> pros_km = H2OKMeansEstimator(categorical_encoding=encoding,
      ...                              seed=1234)
      >>> pros_km.train(x=predictors,
      ...               training_frame=train,
      ...               validation_frame=valid)
      >>> pros_km.scoring_history()
      """
      return self._parms.get("categorical_encoding")
  
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 40 for calling the H2OKMeansEstimator obj's underlying member method code with example usage: obj.categorical_encoding() and returns: self