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: Get parameters for this estimator.
  
  See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
  and sklearn/base.py for more information.
  
  Parameters
  ----------
  deep : boolean, optional
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
docstr_tok: ["Get","parameters","for","this","estimator",".","See","http",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.base.BaseEstimator.html","and","sklearn\/base.py","for","more","information",".","Parameters","--","--","--","--","--","deep",":","boolean",",","optional","If","True",",","will","return","the","parameters","for","this","estimator","and","contained","subobjects","that","are","estimators",".","Returns","--","--","--","-","params",":","mapping","of","string","to","any","Parameter","names","mapped","to","their","values","."]
code: def get_params(self, deep=True):
      """Get parameters for this estimator.
  
      See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
      and sklearn/base.py for more information.
  
      Parameters
      ----------
      deep : boolean, optional
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
  
      out = dict()
      for key in self._get_param_names():
          # We need deprecation warnings to always be on in order to
          # catch deprecated param values.
          # This is set in utils/__init__.py but it gets overwritten
          # when running under python3 somehow.
          warnings.simplefilter("always", DeprecationWarning)
          try:
              with warnings.catch_warnings(record=True) as w:
                  value = getattr(self, key, None)
              if len(w) and w[0].category == DeprecationWarning:
                  # if the parameter is deprecated, don't show it
                  continue
          finally:
              warnings.filters.pop(0)
  
          # XXX: should we rather test if instance of estimator?
          if deep and hasattr(value, "get_params"):
              deep_items = value.get_params().items()
              out.update((key + "__" + k, val) for k, val in deep_items)
          out[key] = value
      return out
  
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 15 for calling the BaseAggregator obj's underlying member method code with example usage: obj.get_params(deep) and returns: out