class_docstr: TargetEncoder
docstr: List of categorical columns or groups of categorical columns to encode. When groups of columns are specified,
  each group is encoded as a single column (interactions are created internally).
  
  Type: ``List[List[str]]``.
docstr_tok: ["List","of","categorical","columns","or","groups","of","categorical","columns","to","encode",".","When","groups","of","columns","are","specified",",","each","group","is","encoded","as","a","single","column","(","interactions","are","created","internally",")",".","Type",":","``","List","[","List","[","str","]","]","``","."]
code: def columns_to_encode(self):
      """
      List of categorical columns or groups of categorical columns to encode. When groups of columns are specified,
      each group is encoded as a single column (interactions are created internally).
  
      Type: ``List[List[str]]``.
      """
      return self._parms.get("columns_to_encode")
  
imports: from h2o.utils.metaclass import deprecated_params, deprecated_property
  import h2o
  import warnings
  from h2o.exceptions import H2ODeprecationWarning
  from h2o.utils.typechecks import U
  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 10 for calling the H2OTargetEncoderEstimator obj's underlying member method code with example usage: obj.columns_to_encode() and returns: self