title: scikit-learn roc_auc_score() returns accuracy values
body: <p>I am trying to compute area under the ROC curve using <code>sklearn.metrics.roc_auc_score</code> using the following method:</p>
  
  <pre><code>roc_auc = sklearn.metrics.roc_auc_score(actual, predicted)
  </code></pre>
  
  <p>where <code>actual</code> is a binary vector with ground truth classification labels and <code>predicted</code> is a binary vector with classification labels that my classifier has predicted.</p>
  
  <p>However, the value of <code>roc_auc</code> that I am getting is EXACTLY similar to accuracy values (proportion of samples whose labels are correctly predicted). This is not a one-off thing. I try my classifier on various values of the parameters and every time I get the same result.</p>
  
  <p>What am I doing wrong here?</p>