class_docstr: A Stacking classifier.
  
  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" and "predict_proba". If None, then the pool of classifiers
      is a bagging classifier.
  
  meta_classifier : object or None, optional (default=None)
      Classifier model used to aggregate the output of the base classifiers.
      If None, a :class:`LogisticRegression` classifier is used.
  
  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`.
  
  passthrough : bool (default=False)
      When False, only the predictions of estimators will be used as
      training data for the meta-classifier. When True, the
      meta-classifier is trained on the predictions as well as the
      original training data.
  
  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
  ----------
  Wolpert, David H. "Stacked generalization." Neural networks 5,
  no. 2 (1992): 241-259.
  
  Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms.
  John Wiley & Sons, 2004.
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code: def __init__(
      self,
      pool_classifiers=None,
      meta_classifier=None,
      passthrough=False,
      random_state=None,
      n_jobs=-1,
  ):
      super(StackedClassifier, self).__init__(
          pool_classifiers=pool_classifiers,
          random_state=random_state,
          n_jobs=n_jobs,
      )
      self.meta_classifier = meta_classifier
      self.passthrough = passthrough
  
file_path: DESlib/deslib/static/stacked.py
imports: import numpy
  from sklearn.linear_model import LogisticRegression
  from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
  from deslib.static.base import BaseStaticEnsemble
label_desc: Use image node_id 1 to create a new StackedClassifier object from inherited base classes: BaseStaticEnsemble with example: obj = StackedClassifier(pool_classifiers, meta_classifier, passthrough, random_state, n_jobs)