class_docstr: Classification method that selects the classifier in the pool with
  highest score to be used for classification. Usually, the performance of
  the single best classifier is estimated based on the validation data.
  
  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`.
  
  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.
docstr: Estimates the posterior probabilities for each class for each sample
  in X. The returned probability estimates for all classes are ordered by
  the label of classes.
  
  Parameters
  ----------
  X : array of shape (n_samples, n_features)
      The data to be classified
  
  Returns
  -------
  predicted_proba : array of shape (n_samples, n_classes)
      Posterior probabilities estimates for each class.
code: def predict_proba(self, X):
      """Estimates the posterior probabilities for each class for each sample
      in X. The returned probability estimates for all classes are ordered by
      the label of classes.
  
      Parameters
      ----------
      X : array of shape (n_samples, n_features)
          The data to be classified
  
      Returns
      -------
      predicted_proba : array of shape (n_samples, n_classes)
          Posterior probabilities estimates for each class.
  
      """
      self._check_is_fitted()
      if "predict_proba" not in dir(self.best_clf_):
          raise ValueError(
              "Base classifier must support the predict_proba function."
          )
      X = check_array(X)
      if self.n_features_ != X.shape[1]:
          raise ValueError(
              "Number of features of the model must "
              "match the input. Model n_features is {0} and "
              "input n_features is {1}."
              "".format(self.n_features_, X.shape[1])
          )
  
      predicted_proba = self.best_clf_.predict_proba(
          X[:, self.estimator_features_[self.best_clf_index_]]
      )
      return predicted_proba
  
file_path: DESlib/deslib/static/single_best.py
imports: import numpy
  from sklearn.metrics import check_scoring
  from sklearn.utils.validation import check_X_y, check_is_fitted, check_array
  from .base import BaseStaticEnsemble
label_desc: Use image node_id 5 for calling the SingleBest obj's underlying member method code with example usage: obj.predict_proba(X) and returns: predicted_proba