class_docstr: A stacking classifier.
  
  A stacking classifier is a classifier 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 = [Classifier(strategy="LightGBM"), Classifier(strategy="RandomForest"),Classifier(strategy="ExtraTrees")]
      List of estimators to fit in the first level using a cross validation.
  
  level_estimator : object, default = LogisticRegression()
      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
  
  drop_first : bool, default = True
      If True, each estimator output n_classes-1 probabilities
  
  random_state : None or 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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code: def get_params(self, deep=True):
      return {
          "level_estimator": self.level_estimator,
          "base_estimators": self.base_estimators,
          "n_folds": self.n_folds,
          "copy": self.copy,
          "drop_first": self.drop_first,
          "random_state": self.random_state,
          "verbose": self.verbose,
      }
  
file_path: MLBox/mlbox/model/classification/stacking_classifier.py
imports: import numpy
  import pandas
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import StratifiedKFold
  from copy import copy
  from .classifier import Classifier
  import warnings
label_desc: Use image node_id 2 for calling the StackingClassifier obj's underlying member method code with example usage: obj.get_params(deep) and returns: dict