class_docstr: Aggregator
docstr: Transformation of training data
  
  Type: ``Literal["none", "standardize", "normalize", "demean", "descale"]``, defaults to ``"normalize"``.
  
  :examples:
  
  >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
  >>> params = {"ignore_const_cols": False,
  ...           "target_num_exemplars": 500,
  ...           "rel_tol_num_exemplars": 0.3,
  ...           "transform": "standardize",
  ...           "categorical_encoding": "eigen"}
  >>> model = H2OAggregatorEstimator(**params)
  >>> model.train(training_frame=df)
  >>> new_df = model.aggregated_frame
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code: def transform(self):
      """
      Transformation of training data
  
      Type: ``Literal["none", "standardize", "normalize", "demean", "descale"]``, defaults to ``"normalize"``.
  
      :examples:
  
      >>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
      >>> params = {"ignore_const_cols": False,
      ...           "target_num_exemplars": 500,
      ...           "rel_tol_num_exemplars": 0.3,
      ...           "transform": "standardize",
      ...           "categorical_encoding": "eigen"}
      >>> model = H2OAggregatorEstimator(**params)
      >>> model.train(training_frame=df)
      >>> new_df = model.aggregated_frame
      """
      return self._parms.get("transform")
  
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 14 for calling the H2OAggregatorEstimator obj's underlying member method code with example usage: obj.transform() and returns: self