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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code: def __init__(
      self,
      base_estimators,
      method="average",
      threshold=0.5,
      weights=None,
      pre_fitted=False,
  ):
      super(SimpleClassifierAggregator, self).__init__(
          base_estimators=base_estimators, pre_fitted=pre_fitted
      )
  
      # validate input parameters
      if method not in [
          "average",
          "maximization",
          "majority_vote",
          "median",
      ]:
          raise ValueError(
              "{method} is not a valid parameter.".format(method=method)
          )
  
      self.method = method
      check_parameter(
          threshold,
          0,
          1,
          include_left=False,
          include_right=False,
          param_name="threshold",
      )
      self.threshold = threshold
  
      # set estimator weights
      self._set_weights(weights)
  
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 1 to create a new SimpleClassifierAggregator object from inherited base classes: BaseAggregator with example: obj = SimpleClassifierAggregator(base_estimators, method, threshold, weights, pre_fitted)