class_docstr: Ensemble model that selects N classifiers with the best performance in a
  dataset
  
  Parameters
  ----------
  pool_classifiers : list of classifiers (Default = None)
      The generated_pool of classifiers trained for the corresponding
      classification problem. Each base classifiers should support the method
      "predict". If None, then the pool of classifiers is a bagging
      classifier.
  
  scoring : string, callable (default = None)
      A single string or a callable to evaluate the predictions on the
      validation set.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  pct_classifiers : float (Default = 0.5)
      Percentage of base classifier that should be selected by the selection
      scheme.
  
  n_jobs : int, default=-1
      The number of parallel jobs to run. None means 1 unless in
      a joblib.parallel_backend context. -1 means using all processors.
      Doesn’t affect fit method.
  
  References
  ----------
  Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. "Dynamic selection
  of classifiers—a comprehensive review."
  Pattern Recognition 47.11 (2014): 3665-3680.
  
  Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms.
  John Wiley & Sons, 2004.
  
  R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier
  selection: Recent advances and perspectives,”
  Information Fusion, vol. 41, pp. 195 – 216, 2018.
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docstr: Checks if each base classifier in the ensemble (selected models)
  implements the predict_proba method.
  
  Raises
  -------
  ValueError
      If the base classifiers do not implements the predict_proba method.
docstr_tok: ["Checks","if","each","base","classifier","in","the","ensemble","(","selected","models",")","implements","the","predict_proba","method",".","Raises","--","--","--","-","ValueError","If","the","base","classifiers","do","not","implements","the","predict_proba","method","."]
code: def _check_predict_proba(self):
      """Checks if each base classifier in the ensemble (selected models)
      implements the predict_proba method.
  
      Raises
      -------
      ValueError
          If the base classifiers do not implements the predict_proba method.
      """
      for clf in self.ensemble_:
          if "predict_proba" not in dir(clf):
              raise ValueError(
                  "All base classifiers should output probability estimates"
              )
  
file_path: DESlib/deslib/static/static_selection.py
imports: import numpy
  from sklearn.metrics import check_scoring
  from sklearn.utils.validation import check_is_fitted, check_X_y, check_array
  from deslib.util.aggregation import majority_voting_rule
  from deslib.util.aggregation import predict_proba_ensemble
  from .base import BaseStaticEnsemble
label_desc: Use image node_id 7 for calling the StaticSelection obj's underlying member method code with example usage: obj._check_predict_proba() without return types