class_docstr: A Stacking regressor.
  
   A stacking regressor is a regressor that uses the predictions of
  several first layer estimators (generated with a cross validation method)
  for a second layer estimator.
  
  
  Parameters
  ----------
  base_estimators : list, default = [Regressor(strategy="LightGBM"),
                                     Regressor(strategy="RandomForest"),
                                     Regressor(strategy="ExtraTrees")]
      List of estimators to fit in the first level using a cross validation.
  
  level_estimator : object, default = LinearRegression()
      The estimator used in second and last level
  
  n_folds : int, default = 5
      Number of folds used to generate the meta features for the training set
  
  copy : bool, default = False
      If true, meta features are added to the original dataset
  
  random_state : None, int or RandomState. default = 1
      Pseudo-random number generator state used for shuffling.
      If None, use default numpy RNG for shuffling.
  
  verbose : bool, default = True
      Verbose mode.
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docstr: Set parameters of a StackingRegressor object.
docstr_tok: ["Set","parameters","of","a","StackingRegressor","object","."]
code: def set_params(self, **params):
      """Set parameters of a StackingRegressor object."""
      self.__fitOK = False
      self.__fittransformOK = False
  
      for k, v in params.items():
          if k not in self.get_params():
              warnings.warn(
                  "Invalid parameter a for stacking_regressor "
                  "StackingRegressor. Parameter IGNORED. Check the"
                  " list of available parameters with "
                  "`stacking_regressor.get_params().keys()`"
              )
          else:
              setattr(self, k, v)
  
file_path: MLBox/mlbox/model/regression/stacking_regressor.py
imports: import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import KFold, cross_val_predict
  from copy import copy
  from .regressor import Regressor
  import warnings
label_desc: Use image node_id 3 for calling the StackingRegressor obj's underlying member method code with example usage: obj.set_params() without return types