title: xgboost predict method returns the same predicted value for all rows
body: <p>I've created an xgboost classifier in Python:</p>
  <p>train is a pandas dataframe with 100k rows and 50 features as columns.
  target is a pandas series</p>
  <pre><code>xgb_classifier = xgb.XGBClassifier(nthread=-1, max_depth=3, silent=0, 
                                     objective='reg:linear', n_estimators=100)
  xgb_classifier = xgb_classifier.fit(train, target)
  
  predictions = xgb_classifier.predict(test)
  </code></pre>
  <p>However, after training, when I use this classifier to predict values the entire results array is the same number. Any idea why this would be happening?</p>
  <p>Data clarification:
  ~50 numerical features with a numerical target</p>
  <p>I've also tried <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html" rel="nofollow noreferrer"><code>RandomForestRegressor</code></a> from sklearn with the same data and it does give realistic predictions. Perhaps a legitimate bug in the xgboost implementation?</p>