class_docstr: Base class for a DS method based on the potential function model.
  All DS methods based on the Potential function should inherit from this
  class.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
docstr: Selects the base classifiers that obtained a competence level higher
  than the predefined threshold. In this case, the threshold indicates
  the competence of the random classifier.
  
  Parameters
  ----------
  competences : array of shape (n_samples, n_classifiers)
      Competence level estimated for each base classifier and test
      example.
  
  Returns
  -------
  selected_classifiers : array of shape (n_samples, n_classifiers)
      Boolean matrix containing True if the base classifier is selected,
      False otherwise.
code: def select(self, competences):
      """Selects the base classifiers that obtained a competence level higher
      than the predefined threshold. In this case, the threshold indicates
      the competence of the random classifier.
  
      Parameters
      ----------
      competences : array of shape (n_samples, n_classifiers)
          Competence level estimated for each base classifier and test
          example.
  
      Returns
      -------
      selected_classifiers : array of shape (n_samples, n_classifiers)
          Boolean matrix containing True if the base classifier is selected,
          False otherwise.
  
      """
      if competences.ndim < 2:
          competences = competences.reshape(1, -1)
  
      # Set the threshold as the performance of the random classifier
      if self.selection_threshold is None:
          selection_threshold = 1.0 / self.n_classes_
      else:
          selection_threshold = self.selection_threshold
  
      selected_classifiers = competences > selection_threshold
      # For the rows that are all False (i.e., no base classifier was
      # selected, select all classifiers (all True)
      selected_classifiers[
          ~np.any(selected_classifiers, axis=1), :
      ] = True
  
      return selected_classifiers
  
file_path: DESlib/deslib/des/probabilistic/base.py
imports: from abc import abstractmethod, ABCMeta
  import numpy
  from deslib.des.base import BaseDES
label_desc: Use image node_id 5 for calling the BaseProbabilistic obj's underlying member method code with example usage: obj.select(competences) and returns: selected_classifiers