title: Changing random state in Logistic Regression doesn't change results
body: <p>Good day everyone! Id like to get a lot of diffrent results and see standard deviation. Here my code:</p>
  
  <pre><code>lr_score, lr_tune_params = auto_tune_lr_second(x_train, x_test, y_train, y_test)
  print('Random state changing...')
  for i in range(1, 151):
      LR = LogisticRegression(C=lr_tune_params[0], max_iter=lr_tune_params[1], penalty=lr_tune_params[2], random_state=i)
      LR.fit(x_train, y_train)
      pred = LR.predict(x_test)
      pr_money_lr.append(find_profit(confusion_matrix(y_test, pred)))
  pr_money_lr = np.array(pr_money_lr)
  lr_mean = pr_money_lr.mean()
  lr_std = pr_money_lr.std()
  print ([lr_mean - lr_std, lr_mean + lr_std])
  </code></pre>
  
  <p>where <code>auto_tune_lr_second(x_train, x_test, y_train, y_test)</code> is the same thing as GridSearch(),
  function <code>find_profit(confusion_matrix(y_test, pred))</code> is hand-made metric for optimization which get confusion matrix and return some scalar result.</p>
  
  <p>In every iteration i change <code>random_state</code> but i get the same results(the same confusion matrix). I tried to set diffrent <code>random_states</code> as a parameter in <code>Logistic Regrassion</code> and tried to set diffrent <code>numpy.random.seed()</code>. None of them worked. Do you have any ideas why i get the same results with diffrent random_states?</p>