title: Different results for XGBoost using python api and scikit-learn wapper
body: <p>Here is example for agaricus sample data:</p>
  
  <pre><code>import xgboost as xgb
  from sklearn.datasets import load_svmlight_files
  
  X_train, y_train, X_test, y_test = load_svmlight_files(('agaricus.txt.train', 'agaricus.txt.test'))
  
  clf = xgb.XGBClassifier()
  param = clf.get_xgb_params()
  clf.fit(X_train, y_train)
  preds_sk = clf.predict_proba(X_test)
  
  dtrain = xgb.DMatrix(X_train, label=y_train)
  dtest = xgb.DMatrix(X_test)
  bst = xgb.train(param, dtrain)
  preds = bst.predict(dtest)
  
  print preds_sk
  print preds
  </code></pre>
  
  <p>And the results are:</p>
  
  <pre><code>[[  9.98860419e-01   1.13956432e-03]
   [  2.97790766e-03   9.97022092e-01]
   [  9.98816252e-01   1.18372787e-03]
   ..., 
   [  1.95205212e-04   9.99804795e-01]
   [  9.98845220e-01   1.15479471e-03]
   [  5.69522381e-04   9.99430478e-01]]
  
  [ 0.21558253  0.7351886   0.21558253 ...,  0.81527805  0.18158565
    0.81527805]
  </code></pre>
  
  <p>Why are the results different? It seems that all default parameter values are the same. And I don't mean here that predict_proba returns <code>[prob, 1- prob]</code>.</p>
  
  <p>xgboost v0.6, scikit-learn v0.18.1, python 2.7.12</p>