title: How to give the same results of accuracy score after the using cross validation?
body: <p>I want to use cross validation for my dataset in Python 3. However, every time I run the code I get the different result of evaluation score. What if I want the same result.</p>
  
  <pre><code>lr = linear_model.LogisticRegression()
  rf = ensemble.RandomForestClassifier(n_estimators = 5, criterion = 'entropy')
  folds = StratifiedKFold(n_splits = 10, shuffle = True, random_state=None)
  
  lr_scoresa = cross_val_score(lr, X, Y, scoring ='accuracy', cv = folds)
  rf_scoresa = cross_val_score(rf, X, Y, scoring ='accuracy', cv = folds)
  rf_scoresf = cross_val_score(rf, X, Y, scoring ='f1', cv = folds)
  
  print(np.mean(rf_scoresa),np.mean(rf_scoresf))
  print(np.mean(lr_scoresa))
  </code></pre>