title: How to provide weighted eval set to XGBClassifier.fit()?
body: <p>From the <a href="http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier.fit" rel="nofollow noreferrer">sklearn-style API of XGBClassifier</a>, we can provide eval examples for early-stopping. </p>
  
  <blockquote>
    <p><strong>eval_set</strong> (list, optional) – A list of (X, y) pairs to use as a
    validation set for early-stopping</p>
  </blockquote>
  
  <p>However, the format only mentions a pair of features and labels. So if the doc is accurate, there is no place to provide weights for these eval examples.</p>
  
  <p>Am I missing anything?</p>
  
  <p>If it's not achievable in the sklearn-style, is it supported in the original (i.e. non-sklearn) XGBClassifier API? A short example will be nice, since I never used that version of the API.</p>