class_docstr: Estimates the drift between two datasets
  
      
  Parameters
  ----------
  estimator : classifier, defaut = RandomForestClassifier(n_estimators = 50, n_jobs=-1, max_features=1., min_samples_leaf = 5, max_depth = 5)
      The estimator that estimates the drift between two datasets
      
  n_folds : int, defaut = 2
      Number of folds used to estimate the drift
  
  stratify : bool, defaut = True
      Whether the cv is stratified (same number of train and test samples within each fold)
  
  random_state : int, defaut = 1
      Random state for cv
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code: def __init__(
      self,
      estimator=RandomForestClassifier(
          n_estimators=50,
          n_jobs=-1,
          max_features=1.0,
          min_samples_leaf=5,
          max_depth=5,
      ),
      n_folds=2,
      stratify=True,
      random_state=1,
  ):
      self.estimator = estimator
      self.n_folds = n_folds
      self.stratify = stratify
      self.random_state = random_state
      self.__cv = None
      self.__pred = None
      self.__target = None
      self.__fitOK = False
  
file_path: MLBox/mlbox/preprocessing/drift/drift_estimator.py
imports: import numpy
  import pandas
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import KFold, StratifiedKFold, cross_val_predict
label_desc: Use image node_id 1 to create a new DriftEstimator object  with example: obj = DriftEstimator(estimator, n_folds, stratify, random_state)