title: Computing AUC and ROC curve from multi-class data in scikit-learn (sklearn)?
body: <p>I am trying to use the <code>scikit-learn</code> module to compute AUC and plot ROC curves for the output of three different classifiers to compare their performance.  I am very new to this topic, and I am struggling to understand how the data I have should input to the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" rel="noreferrer"><code>roc_curve</code></a> and <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc" rel="noreferrer"><code>auc</code></a> functions.</p>
  
  <p>For each item within the testing set, I have the true value and the output of each of the three classifiers. The classes are <code>['N', 'L', 'W', 'T']</code>.  In addition, I have a confidence score for each value output from the classifiers.  How do I pass this information to the roc_curve function?</p>
  
  <p>Do I need to <code>label_binarize</code> my input data?  How do I convert a list of <code>[class, confidence]</code> pairs output by the classifiers into the <code>y_score</code> expected by <code>roc_curve</code>?</p>
  
  <p>Thank you for any help! Good resources about ROC curves would also be helpful.</p>