class_docstr: TargetEncoder
docstr: If true, enables blending of posterior probabilities (computed for a given categorical value) with prior
  probabilities (computed on the entire set). This allows to mitigate the effect of categorical values with small
  cardinality. The blending effect can be tuned using the `inflection_point` and `smoothing` parameters.
  
  Type: ``bool``, defaults to ``False``.
  
  :examples:
  
  >>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
  >>> predictors = ["home.dest", "cabin", "embarked"]
  >>> response = "survived"
  >>> titanic["survived"] = titanic["survived"].asfactor()
  >>> fold_col = "kfold_column"
  >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
  >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
  ...                                        smoothing=25,
  ...                                        blending=True)
  >>> titanic_te.train(x=predictors,
  ...                  y=response,
  ...                  training_frame=titanic)
  >>> titanic_te
docstr_tok: ["If","true",",","enables","blending","of","posterior","probabilities","(","computed","for","a","given","categorical","value",")","with","prior","probabilities","(","computed","on","the","entire","set",")",".","This","allows","to","mitigate","the","effect","of","categorical","values","with","small","cardinality",".","The","blending","effect","can","be","tuned","using","the","`","inflection_point","`","and","`","smoothing","`","parameters",".","Type",":","``","bool","``",",","defaults","to","``","False","``",".",":","examples",":",">",">",">","titanic","=","h2o.import_file","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/gbm_test\/titanic.csv","''",")",">",">",">","predictors","=","[","``","home.dest","''",",","``","cabin","''",",","``","embarked","''","]",">",">",">","response","=","``","survived","''",">",">",">","titanic","[","``","survived","''","]","=","titanic","[","``","survived","''","]",".asfactor","(",")",">",">",">","fold_col","=","``","kfold_column","''",">",">",">","titanic","[","fold_col","]","=","titanic.kfold_column","(","n_folds=5",",","seed=1234",")",">",">",">","titanic_te","=","H2OTargetEncoderEstimator","(","inflection_point=35",",","...","smoothing=25",",","...","blending=True",")",">",">",">","titanic_te.train","(","x=predictors",",","...","y=response",",","...","training_frame=titanic",")",">",">",">","titanic_te"]
code: def blending(self):
      """
      If true, enables blending of posterior probabilities (computed for a given categorical value) with prior
      probabilities (computed on the entire set). This allows to mitigate the effect of categorical values with small
      cardinality. The blending effect can be tuned using the `inflection_point` and `smoothing` parameters.
  
      Type: ``bool``, defaults to ``False``.
  
      :examples:
  
      >>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
      >>> predictors = ["home.dest", "cabin", "embarked"]
      >>> response = "survived"
      >>> titanic["survived"] = titanic["survived"].asfactor()
      >>> fold_col = "kfold_column"
      >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
      >>> titanic_te = H2OTargetEncoderEstimator(inflection_point=35,
      ...                                        smoothing=25,
      ...                                        blending=True)
      >>> titanic_te.train(x=predictors,
      ...                  y=response,
      ...                  training_frame=titanic)
      >>> titanic_te
      """
      return self._parms.get("blending")
  
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 14 for calling the H2OTargetEncoderEstimator obj's underlying member method code with example usage: obj.blending() and returns: self