title: Difference between original xgboost (Learning API) and sklearn XGBClassifier (Scikit-Learn API)
body: <p>I use the xgboots sklearn interface below to create and train an xgb model-1.</p>
  
  <pre><code>clf = xgb.XGBClassifier(n_estimators = 100, objective= 'binary:logistic',)
  clf.fit(x_train, y_train,  early_stopping_rounds=10, eval_metric="auc", 
      eval_set=[(x_valid, y_valid)])
  </code></pre>
  
  <p>And the xgboost model can be created by original xgboost as model-2 below:</p>
  
  <pre><code>param = {}
  param['objective'] = 'binary:logistic'
  param['eval_metric'] = "auc"
  num_rounds = 100
  xgtrain = xgb.DMatrix(x_train, label=y_train)
  xgval = xgb.DMatrix(x_valid, label=y_valid)
  watchlist = [(xgtrain, 'train'),(xgval, 'val')]
  model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=10)
  </code></pre>
  
  <p>I think all the parameters are the same between model-1 and model-2. But the validation score is different. Is any difference between model-1 and model-2 ?</p>