title: Setting random state for cross_val_score doesn't work
body: <p>I am attempting to run a 3x 10-fold cross validation. Problem is I keep getting the same results from the three different iterations which tells me that the folds are not splitting differently. Why is that and how can I make this to be random each time (or at least based on i)</p>
  
  <pre><code>from sklearn.ensemble import AdaBoostClassifier
  from sklearn.datasets import make_classification
  from sklearn.model_selection import cross_val_score
  import statistics as stats
  import random
  import numpy as np
  
  X, y = make_classification(n_samples=1000, n_features=4,
                             n_informative=2, n_redundant=0,
                             random_state=0, shuffle=False)
  print(X)
  print(y)
  
  
  CVScores = []
  for i in range(0, 3):
      np.random.seed(i)
      ada = AdaBoostClassifier(n_estimators=100, random_state=np.random.RandomState(i))
      ada.fit(X, y)
      CVScores.extend(list(cross_val_score(ada, X, y, cv=10, scoring="f1")))
  print(CVScores)
  mean = stats.mean(CVScores)
  print(mean)
  </code></pre>