title: R: xgboost plot roc curve
body: <p>To plot roc curve:</p>
  
  <pre><code>library(ROCR)
  &lt;data cleaning/scrubbing&gt;
  &lt;train data&gt;
  .....
  .....
  rf.perf = performance(rf.prediction, "tpr", "fpr") #for RF
  logit.perf = performance (logit.prediction, "tpr", "fpr") #for logistic reg
  tree.perf = performance(tree.prediction, "tpr", "fpr") #for cart tree
  ...
  plot(re.perf) #a RF roc curve
  </code></pre>
  
  <p>If I want to run a <code>xgboost</code> classification and subsequently plotting roc:
  objective = "binary:logistics"</p>
  
  <p>I'm confused with the xgboost's arguments <em>metrics "auc"</em> (page 9 of the <a href="https://cran.r-project.org/web/packages/xgboost/xgboost.pdf" rel="nofollow">CRAN manual</a>), it says area. 
  How does one plot the curve with tpr and fpr for model comparison?</p>
  
  <p>I tried search the net and github, most emphasis on feature importance graph (for <code>xgboost</code>).</p>
  
  <p>Thanks</p>