class_docstr: A collection of simple classifier combination methods.
  
  Parameters
  ----------
  base_estimators: list or numpy array (n_estimators,)
      A list of base classifiers.
  
  method : str, optional (default='average')
      Combination method: {'average', 'maximization', 'majority vote',
      'median'}. Pass in weights of classifier for weighted version.
  
  threshold : float in (0, 1), optional (default=0.5)
      Cut-off value to convert scores into binary labels.
  
  weights : numpy array of shape (1, n_classifiers)
      Classifier weights.
  
  pre_fitted : bool, optional (default=False)
      Whether the base classifiers are trained. If True, `fit`
      process may be skipped.
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docstr: Fit classifier.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  y : numpy array of shape (n_samples,), optional (default=None)
      The ground truth of the input samples (labels).
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code: def fit(self, X, y):
      """Fit classifier.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      y : numpy array of shape (n_samples,), optional (default=None)
          The ground truth of the input samples (labels).
      """
  
      # Validate inputs X and y
      X, y = check_X_y(X, y)
      X = check_array(X)
      self._set_n_classes(y)
  
      if self.pre_fitted:
          print("Training skipped")
          return
      else:
          for clf in self.base_estimators:
              clf.fit(X, y)
              clf.fitted_ = True
          return
  
file_path: combo/combo/models/classifier_comb.py
imports: import numpy
  from sklearn.utils import check_array
  from sklearn.utils import check_X_y
  from sklearn.utils import column_or_1d
  from pyod.utils.utility import check_parameter
  from .base import BaseAggregator
  from .score_comb import average, maximization, majority_vote, median
  from ..utils.utility import score_to_proba
label_desc: Use image node_id 2 for calling the SimpleClassifierAggregator obj's underlying member method code with example usage: obj.fit(X, y) without return types