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.
code: def __init__(
      self,
      pool_classifiers=None,
      scoring=None,
      random_state=None,
      n_jobs=-1,
  ):
      super(SingleBest, self).__init__(
          pool_classifiers=pool_classifiers,
          random_state=random_state,
          n_jobs=n_jobs,
      )
      self.scoring = scoring
  
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 1 to create a new SingleBest object from inherited base classes: BaseStaticEnsemble with example: obj = SingleBest(pool_classifiers, scoring, random_state, n_jobs)