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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docstr: Fit the model by training a meta-classifier on the outputs of the
  base classifiers
  
  Parameters
  ----------
  X : array of shape (n_samples, n_features)
      Data used to fit the model.
  
  y : array of shape (n_samples)
      class labels of each example in X.
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code: def fit(self, X, y):
      """Fit the model by training a meta-classifier on the outputs of the
      base classifiers
  
      Parameters
      ----------
      X : array of shape (n_samples, n_features)
          Data used to fit the model.
  
      y : array of shape (n_samples)
          class labels of each example in X.
  
      """
      X, y = self._validate_data(
          X,
          y,
          accept_sparse="csr",
          dtype=np.float64,
          order="C",
          accept_large_sparse=False,
      )
      super(StackedClassifier, self).fit(X, y)
      base_preds = self._predict_proba_base(X)
      X_meta = self._connect_input(X, base_preds)
  
      # Prepare the meta-classifier
      if self.meta_classifier is None:
          self.meta_classifier_ = LogisticRegression(
              solver="lbfgs",
              multi_class="auto",
              max_iter=1000,
              random_state=self.random_state_,
          )
  
      else:
          self.meta_classifier_ = self.meta_classifier
  
      self.meta_classifier_.fit(X_meta, self.y_enc_)
  
      return self
  
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 2 for calling the StackedClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self