class_docstr: AdaBoost
  
  Builds an AdaBoost model
class_docstr_tok: ["AdaBoost","Builds","an","AdaBoost","model"]
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"``.
docstr_tok: ["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","''","``","."]
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"``.
      """
      return self._parms.get("categorical_encoding")
  
imports: import ast
  import json
  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
  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 8 for calling the H2OAdaBoostEstimator obj's underlying member method code with example usage: obj.categorical_encoding() and returns: self