title: How to calculate ROC_AUC score having 3 classes
body: <p>I have a data having 3 class labels(0,1,2). I tried to make ROC curve. and did it by using pos_label parameter.</p>
  
  <pre><code>fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0)
  </code></pre>
  
  <p>By changing pos_label to 0,1,2- I get 3 graphs, Now I am having issue in calculating AUC score. 
  How can I average the 3 graphs and plot 1 graph from it and then calculate the Roc_AUC score.
  i am having error in by this
      metrics.roc_auc_score(Ytest, y_pred_prob)</p>
  
  <p>ValueError: multiclass format is not supported</p>
  
  <p>please help me. </p>
  
  <pre><code># store the predicted probabilities for class 0
  y_pred_prob = cls.predict_proba(Xtest)[:, 0]
  #first argument is true values, second argument is predicted probabilities
  fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0)
  plt.plot(fpr, tpr)
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.0])
  plt.title('ROC curve classifier')
  plt.xlabel('False Positive Rate (1 - Specificity)')
  plt.ylabel('True Positive Rate (Sensitivity)')
  plt.grid(True)
  </code></pre>
  
  <hr>
  
  <pre><code># store the predicted probabilities for class 1
  y_pred_prob = cls.predict_proba(Xtest)[:, 1]
  #first argument is true values, second argument is predicted probabilities
  fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0)
  plt.plot(fpr, tpr)
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.0])
  plt.title('ROC curve classifier')
  plt.xlabel('False Positive Rate (1 - Specificity)')
  plt.ylabel('True Positive Rate (Sensitivity)')
  </code></pre>
  
  <h2>    plt.grid(True)</h2>
  
  <pre><code># store the predicted probabilities for class 2
  y_pred_prob = cls.predict_proba(Xtest)[:, 2]
  #first argument is true values, second argument is predicted probabilities
  fpr, tpr, thresholds = metrics.roc_curve(Ytest, y_pred_prob, pos_label = 0)
  plt.plot(fpr, tpr)
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.0])
  plt.title('ROC curve classifier')
  plt.xlabel('False Positive Rate (1 - Specificity)')
  plt.ylabel('True Positive Rate (Sensitivity)')
  </code></pre>
  
  <h2>    plt.grid(True)</h2>
  
  <p>from the above code. 3 roc curves are generated. Due to multi-classes.</p>
  
  <p>I want to have a one roc curve from above 3 by taking average or mean. Then, one roc_auc score from that.</p>