class_docstr: Estimate the univariate drift between two datasets.
  
  Estimate the univariate drift between two datasets
  and select features with low drifts
  
  Parameters
  ----------
  threshold : float, defaut = 0.6
      The drift threshold (univariate drift below are kept)
      Must be between 0. and 1.
  
  subsample : float, defaut = 1.
      Subsampling parameter for the datasets.
      Must be between 0. and 1.
  
  estimator : classifier, default = DecisionTreeClassifier(max_depth=6)
      The estimator that estimates the drift between two datasets.
  
  n_folds : int, default = 2
      Number of folds used to estimate the drift.
  
  stratify : bool, default = True
      Whether the cv is stratified (same number of train and test samples
      within each fold)
  
  random_state : int, default = 1
      Seed for for cv and subsampling.
  
  n_jobs : int, defaut = -1
      Number of cores used for processing (-1 for all cores)
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docstr: Return the variables kept or dropped.
  
  Parameters
  ----------
  complement : bool, default = True
      If True, returns the features to drop
      If False, returns the features to keep
  
  Returns
  -------
  list
      The list of features to keep or to drop.
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code: def get_support(self, complement=False):
      """Return the variables kept or dropped.
  
      Parameters
      ----------
      complement : bool, default = True
          If True, returns the features to drop
          If False, returns the features to keep
  
      Returns
      -------
      list
          The list of features to keep or to drop.
  
      """
      if self.__fitOK:
          keepList = []
          dropList = []
  
          for col in self.__Ddrifts:
              if self.__Ddrifts[col] < self.threshold:
                  keepList.append(col)
              else:
                  dropList.append(col)
  
          if complement:
              return dropList
          else:
              return keepList
      else:
          raise ValueError("Call the fit function before !")
  
file_path: MLBox/mlbox/preprocessing/drift/drift_threshold.py
imports: import sys
  from joblib import Parallel, delayed
  from sklearn.tree import DecisionTreeClassifier
  from .drift_estimator import DriftEstimator
label_desc: Use image node_id 6 for calling the DriftThreshold obj's underlying member method code with example usage: obj.get_support(complement) and returns: dropList, keepList