title: What is the difference between cross_val_score with scoring='roc_auc' and roc_auc_score?
body: <p>I am confused about the difference between the cross_val_score scoring metric 'roc_auc' and the roc_auc_score that I can just import and call directly. </p>
  
  <p>The documentation (<a href="http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>) indicates that specifying scoring='roc_auc' will use the sklearn.metrics.roc_auc_score. However, when I implement GridSearchCV or cross_val_score with scoring='roc_auc' I receive very different numbers that when I call roc_auc_score directly.</p>
  
  <p>Here is my code to help demonstrate what I see:</p>
  
  <pre><code># score the model using cross_val_score
  
  rf = RandomForestClassifier(n_estimators=150,
                              min_samples_leaf=4,
                              min_samples_split=3,
                              n_jobs=-1)
  
  scores = cross_val_score(rf, X, y, cv=3, scoring='roc_auc')
  
  print scores
  array([ 0.9649023 ,  0.96242235,  0.9503313 ])
  
  # do a train_test_split, fit the model, and score with roc_auc_score
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
  rf.fit(X_train, y_train)
  
  print roc_auc_score(y_test, rf.predict(X_test))
  0.84634039111363313 # quite a bit different than the scores above!
  </code></pre>
  
  <p>I feel like I am missing something very simple here -- most likely a mistake in how I am implementing/interpreting one of the scoring metrics.</p>
  
  <p>Can anyone shed any light on the reason for the discrepancy between the two scoring metrics?</p>