title: Saving xgboost binary prediction to submission csv file
body: <p>I have 'train.csv' and 'test.csv' files. The former contains 'Id', a list of features, and a 'Status' column with values in it, the 'test.csv' file contains the same columns except the 'Status' one. </p>
  
  <p>My task is to train an XGboost model on the 'train.csv' file and predict binary outcome of 'Status' for the 'test.csv' file, then to save 'Id' and 'Status' to a separate csv file for submission.</p>
  
  <p>I am able to train XGboost on the 'train' file, and the roc_auc score is pretty good (above 0.8). I have spent hours searching the internet how to make predictions for the 'test' file and save them to the 'submission' file. To my surprise, and although this is quite a common task, I couldn't find any scripts that would reliably perform the operations described above. </p>
  
  <p>My working code for the 'train.csv' file just in case:</p>
  
  <pre><code>predict = pd.read_csv("train.csv")
  predictors =['par48','par52','par75','par82','par84','par85','par86','par87','par89','par108','par109','par132','par156','par165','par167','par175','par190','par197']
  X, y = predict[predictors], predict['Status']
  X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=123)
  xg_cl=xgb.XGBClassifier(objective='binary:logistic',n_estimators=10,seed=123)
  xg_cl.fit(X_train, y_train)
  preds=xg_cl.predict(X_test)
  accuracy = float(np.sum(preds==y_test))/y_test.shape[0]
  print("accuracy: %f" % (accuracy))
  print(xg_cl.feature_importances_)
  print(roc_auc_score(y_test, xg_cl.predict_proba(X_test)[:,1]))
  </code></pre>
  
  <p>Do you have a working code to share? Thanks!</p>