title: Different result roc_auc_score and plot_roc_curve
body: <p>I am training a <code>RandomForestClassifier</code> (sklearn) to predict credit card fraud. When I then test the model and check the rocauc score i get different values when I use <code>roc_auc_score</code> and <code>plot_roc_curve</code>. roc_auc_score gives me around 0.89 and the plot_curve calculates AUC to 0.96 why is that? </p>
  
  <p>The labels are all 0 and 1 as well as the predictions are 0 or 1. 
  CodE:</p>
  
  <pre><code>clf = RandomForestClassifier(random_state =42)
  clf.fit(X_train, y_train[target].values)
  pred_test = clf.predict(X_test)
  print(roc_auc_score(y_test, pred_test))
  clf_disp = plot_roc_curve(clf, X_test, y_test)
  plt.show()
  </code></pre>
  
  <p>Output of the code (the roc_auc_Score is just above the graph).</p>
  
  <p><img src="https://i.stack.imgur.com/uOeOs.png" alt="image"></p>