title: What is the meaning of 'mean_test_score' in cv_result?
body: <p>Hello I'm doing a GridSearchCV and I'm printing the result with the <code>.cv_results_</code> function from <code>scikit learn</code>. </p>
  
  <p>My problem is that when I'm evaluating by hand the mean on all the test score splits I obtain a different number compared to what it is written in <code>'mean_test_score'</code>. Which is different from the standard <code>np.mean()</code>?</p>
  
  <p>I attach here the code with the result:</p>
  
  <pre><code>n_estimators = [100]
  max_depth = [3]
  learning_rate = [0.1]
  
  param_grid = dict(max_depth=max_depth, n_estimators=n_estimators, learning_rate=learning_rate)
  
  gkf = GroupKFold(n_splits=7)
  
  
  grid_search = GridSearchCV(model, param_grid, scoring=score_auc, cv=gkf)
  grid_result = grid_search.fit(X, Y, groups=patients)
  
  grid_result.cv_results_
  </code></pre>
  
  <p>The result of this operation is:</p>
  
  <pre><code>{'mean_fit_time': array([ 8.92773601]),
   'mean_score_time': array([ 0.04288721]),
   'mean_test_score': array([ 0.83490629]),
   'mean_train_score': array([ 0.95167036]),
   'param_learning_rate': masked_array(data = [0.1],
                mask = [False],
          fill_value = ?),
   'param_max_depth': masked_array(data = [3],
                mask = [False],
          fill_value = ?),
   'param_n_estimators': masked_array(data = [100],
                mask = [False],
          fill_value = ?),
   'params': ({'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100},),
   'rank_test_score': array([1]),
   'split0_test_score': array([ 0.74821666]),
   'split0_train_score': array([ 0.97564995]),
   'split1_test_score': array([ 0.80089016]),
   'split1_train_score': array([ 0.95361201]),
   'split2_test_score': array([ 0.92876979]),
   'split2_train_score': array([ 0.93935856]),
   'split3_test_score': array([ 0.95540287]),
   'split3_train_score': array([ 0.94718634]),
   'split4_test_score': array([ 0.89083901]),
   'split4_train_score': array([ 0.94787374]),
   'split5_test_score': array([ 0.90926355]),
   'split5_train_score': array([ 0.94829775]),
   'split6_test_score': array([ 0.82520379]),
   'split6_train_score': array([ 0.94971417]),
   'std_fit_time': array([ 1.79167576]),
   'std_score_time': array([ 0.02970254]),
   'std_test_score': array([ 0.0809713]),
   'std_train_score': array([ 0.0105566])}
  </code></pre>
  
  <p>As you can see, doing the <code>np.mean</code> of all the test_score it gives you a value approximately of 0.8655122606479532 while the 'mean_test_score' is 0.83490629</p>
  
  <p>Thanks for you help,
  Leonardo.</p>