title: ROC curve for LinearSVC
body: <p>I'm currently struggling plotting a ROC curve for a <code>LinearSVC</code> model. Since <code>LinearSVC</code> models can only call <code>decision_function()</code> to calculate the y_score (as opposed to the usual <code>predict_proba()</code>), I find it diffcult to compute <code>fpr</code> and <code>tpr</code> for each class. When trying</p>
  
  <pre><code>for i in range(n_classes):
      fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
      roc_auc[i] = auc(fpr[i], tpr[i])
  </code></pre>
  
  <p>I get <code>IndexError: too many indices for array</code>. Switching to the solution provided in <a href="https://stackoverflow.com/questions/45332410/sklearn-roc-for-multiclass-classification">this</a> answer would imply binarising the labels, which I would like to avoid. A normal <code>SVC</code> model with linear kernel wouldn't allow me to set an l1 penalty for l1-based feature selection, so that's to be avoided as well. </p>
  
  <p>Any ideas on how to solve this?</p>