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 set_params(self, **params):
      if "estimator" in params.keys():
          self.estimator = params["estimator"]
      if "n_folds" in params.keys():
          self.n_folds = params["n_folds"]
      if "stratify" in params.keys():
          self.stratify = params["stratify"]
      if "random_state" in params.keys():
          self.random_state = params["random_state"]
  
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 3 for calling the DriftEstimator obj's underlying member method code with example usage: obj.set_params() without return types