class_docstr: Abstract class for all combination classes.
  
  Parameters
  ----------
  base_estimators: list, length must be greater than 1
      A list of base estimators. Certain methods must be present, e.g.,
      `fit` and `predict`.
  
  pre_fitted: bool, optional (default=False)
      Whether the base estimators are trained. If True, `fit`
      process may be skipped.
docstr: Set the parameters of this estimator.
  The method works on simple estimators as well as on nested objects
  (such as pipelines). The latter have parameters of the form
  ``<component>__<parameter>`` so that it's possible to update each
  component of a nested object.
  
  See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
  and sklearn/base.py for more information.
  
  Returns
  -------
  self : object
docstr_tok: ["Set","the","parameters","of","this","estimator",".","The","method","works","on","simple","estimators","as","well","as","on","nested","objects","(","such","as","pipelines",")",".","The","latter","have","parameters","of","the","form","``","<","component",">","__","<","parameter",">","``","so","that","it","'s","possible","to","update","each","component","of","a","nested","object",".","See","http",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.base.BaseEstimator.html","and","sklearn\/base.py","for","more","information",".","Returns","--","--","--","-","self",":","object"]
code: def set_params(self, **params):
      # noinspection PyPep8
      """Set the parameters of this estimator.
      The method works on simple estimators as well as on nested objects
      (such as pipelines). The latter have parameters of the form
      ``<component>__<parameter>`` so that it's possible to update each
      component of a nested object.
  
      See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
      and sklearn/base.py for more information.
  
      Returns
      -------
      self : object
      """
  
      if not params:
          # Simple optimization to gain speed (inspect is slow)
          return self
      valid_params = self.get_params(deep=True)
  
      nested_params = defaultdict(dict)  # grouped by prefix
      for key, value in params.items():
          key, delim, sub_key = key.partition("__")
          if key not in valid_params:
              raise ValueError(
                  "Invalid parameter %s for estimator %s. "
                  "Check the list of available parameters "
                  "with `estimator.get_params().keys()`." % (key, self)
              )
  
          if delim:
              nested_params[key][sub_key] = value
          else:
              setattr(self, key, value)
  
      for key, sub_params in nested_params.items():
          valid_params[key].set_params(**sub_params)
  
      return self
  
imports: import warnings
  from collections import defaultdict
  from abc import ABC, abstractmethod
  import numpy
  from numpy import percentile
  from scipy.special import erf
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils import column_or_1d
  from sklearn.utils.validation import check_is_fitted
  from sklearn.utils.multiclass import check_classification_targets
  from .sklearn_base import _pprint
  from inspect import signature
label_desc: Use image node_id 16 for calling the BaseAggregator obj's underlying member method code with example usage: obj.set_params() and returns: self, self