class_docstr: Aggregator
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:
  
  >>> df = h2o.create_frame(rows=10000,
  ...                       cols=10,
  ...                       categorical_fraction=0.6,
  ...                       integer_fraction=0,
  ...                       binary_fraction=0,
  ...                       real_range=100,
  ...                       integer_range=100,
  ...                       missing_fraction=0,
  ...                       factors=100,
  ...                       seed=1234)
  >>> params = {"target_num_exemplars": 1000,
  ...           "rel_tol_num_exemplars": 0.5,
  ...           "categorical_encoding": "eigen"}
  >>> agg = H2OAggregatorEstimator(**params)
  >>> agg.train(training_frame=df)
  >>> new_df = agg.aggregated_frame
  >>> new_df
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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:
  
      >>> df = h2o.create_frame(rows=10000,
      ...                       cols=10,
      ...                       categorical_fraction=0.6,
      ...                       integer_fraction=0,
      ...                       binary_fraction=0,
      ...                       real_range=100,
      ...                       integer_range=100,
      ...                       missing_fraction=0,
      ...                       factors=100,
      ...                       seed=1234)
      >>> params = {"target_num_exemplars": 1000,
      ...           "rel_tol_num_exemplars": 0.5,
      ...           "categorical_encoding": "eigen"}
      >>> agg = H2OAggregatorEstimator(**params)
      >>> agg.train(training_frame=df)
      >>> new_df = agg.aggregated_frame
      >>> new_df
      """
      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 16 for calling the H2OAggregatorEstimator obj's underlying member method code with example usage: obj.categorical_encoding() and returns: self