class_docstr: Meta-estimator to set a custom probability threshold.
docstr: Set the parameters of this estimator.
  
  The method works on simple estimators as well as on nested objects
  (such as pipelines). The latter have parameters of the form
  ``<component>__<parameter>`` so that it's possible to update each
  component of a nested object.
  
  Parameters
  ----------
  **params : dict
      Estimator parameters.
  
  Returns
  -------
  self : object
      Estimator instance.
docstr_tok: ["Set","the","parameters","of","this","estimator",".","The","method","works","on","simple","estimators","as","well","as","on","nested","objects","(","such","as","pipelines",")",".","The","latter","have","parameters","of","the","form","``","<","component",">","__","<","parameter",">","``","so","that","it","'s","possible","to","update","each","component","of","a","nested","object",".","Parameters","--","--","--","--","--","*","*","params",":","dict","Estimator","parameters",".","Returns","--","--","--","-","self",":","object","Estimator","instance","."]
code: def set_params(self, **params):
      """
      Set the parameters of this estimator.
  
      The method works on simple estimators as well as on nested objects
      (such as pipelines). The latter have parameters of the form
      ``<component>__<parameter>`` so that it's possible to update each
      component of a nested object.
  
      Parameters
      ----------
      **params : dict
          Estimator parameters.
  
      Returns
      -------
      self : object
          Estimator instance.
      """
      if "classifier" in params:
          self.classifier = params.pop("classifier")
      if "probability_threshold" in params:
          self.probability_threshold = params.pop(
              "probability_threshold"
          )
      return self.classifier.set_params(**params)
  
imports: import sklearn.compose
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.compose import TransformedTargetRegressor
  from sklearn.preprocessing import PowerTransformer
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 5 for calling the CustomProbabilityThresholdClassifier obj's underlying member method code with example usage: obj.set_params() and returns: self