class_docstr: TargetEncoder
docstr: Apply transformation to `te_columns` based on the encoding maps generated during `train()` method call.
  
  :param H2OFrame frame: the frame on which to apply the target encoding transformations.
  :param boolean blending: If provided, this overrides the `blending` parameter on the model.
  :param float inflection_point: If provided, this overrides the `inflection_point` parameter on the model.
  :param float smoothing: If provided, this overrides the `smoothing` parameter on the model.
  :param float noise: If provided, this overrides the amount of random noise added to the target encoding defined on the model, this helps prevent overfitting.
  :param boolean as_training: Must be set to True when encoding the training frame. 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[response] = titanic[response].asfactor()
  >>> fold_col = "kfold_column"
  >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
  >>> titanic_te = H2OTargetEncoderEstimator(data_leakage_handling="leave_one_out",
  ...                                        inflection_point=35,
  ...                                        smoothing=25,
  ...                                        blending=True,
  ...                                        seed=1234)
  >>> titanic_te.train(x=predictors,
  ...                  y=response,
  ...                  training_frame=titanic)
  >>> transformed = titanic_te.transform(frame=titanic)
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code: def transform(
      self,
      frame,
      blending=None,
      inflection_point=None,
      smoothing=None,
      noise=None,
      as_training=False,
      **kwargs,
  ):
      """
      Apply transformation to `te_columns` based on the encoding maps generated during `train()` method call.
  
      :param H2OFrame frame: the frame on which to apply the target encoding transformations.
      :param boolean blending: If provided, this overrides the `blending` parameter on the model.
      :param float inflection_point: If provided, this overrides the `inflection_point` parameter on the model.
      :param float smoothing: If provided, this overrides the `smoothing` parameter on the model.
      :param float noise: If provided, this overrides the amount of random noise added to the target encoding defined on the model, this helps prevent overfitting.
      :param boolean as_training: Must be set to True when encoding the training frame. 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[response] = titanic[response].asfactor()
      >>> fold_col = "kfold_column"
      >>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
      >>> titanic_te = H2OTargetEncoderEstimator(data_leakage_handling="leave_one_out",
      ...                                        inflection_point=35,
      ...                                        smoothing=25,
      ...                                        blending=True,
      ...                                        seed=1234)
      >>> titanic_te.train(x=predictors,
      ...                  y=response,
      ...                  training_frame=titanic)
      >>> transformed = titanic_te.transform(frame=titanic)
      """
      for k in kwargs:
          if k in ["seed", "data_leakage_handling"]:
              warnings.warn(
                  "`%s` is deprecated in `transform` method and will be ignored. "
                  "Instead, please ensure that it was set before training on the H2OTargetEncoderEstimator model."
                  % k,
                  H2ODeprecationWarning,
              )
          else:
              raise TypeError(
                  "transform() got an unexpected keyword argument '%s'"
                  % k
              )
  
      if "data_leakage_handling" in kwargs:
          dlh = kwargs["data_leakage_handling"]
          assert_is_type(
              dlh, None, Enum("leave_one_out", "k_fold", "none")
          )
          if dlh is not None and dlh.lower() != "none":
              warnings.warn(
                  "Deprecated `data_leakage_handling=%s` is replaced by `as_training=True`. "
                  "Please update your code." % dlh,
                  H2ODeprecationWarning,
              )
              as_training = True
  
      params = dict(
          model=self.model_id,
          frame=frame.key,
          blending=blending
          if blending is not None
          else self.blending,  # always need to provide blending here as we can't represent unset value
          inflection_point=inflection_point,
          smoothing=smoothing,
          noise=noise,
          as_training=as_training,
      )
  
      output = h2o.api("GET /3/TargetEncoderTransform", data=params)
      return h2o.get_frame(output["name"])
  
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 26 for calling the H2OTargetEncoderEstimator obj's underlying member method code with example usage: obj.transform(frame, blending, inflection_point, smoothing, noise, as_training) and returns: h2o