title: Problem regarding predict_proba function in XGBoost in python
body: <p>Currently I am working on a binary classification problem.
  I want my predicted output to be the probability, not 1 or 0 using XGBoost.</p>
  
  <p>I have divided the data set into train, validate and test set.</p>
  
  <pre class="lang-py prettyprint-override"><code>global label, id_column, features
  label = 'is_default'
  id_column = 'emp_id'
  features = ['age', 'income', 'dependent','A','B','C']
  
  train, valid, test = np.split(df.sample(frac=1), [int(.8*len(df)), int(.95*len(df))])
  
  X_train, y_train = train[features], train[label]
  X_valid, y_valid = valid[features], valid[label]
  X_test, y_test = test[features], test[label]
  
  params = {
   'num_class' : 2,
   'learning_rate' : 0.1,
   'n_estimators':5,
   'max_depth':5,
   'min_child_weight':1,
   'gamma':2,
   'subsample':0.8,
   'colsample_bytree':0.5,
   'objective':'multi:softprob',
   'scale_pos_weight':2.14,
   'nthread':4,
   'seed':27}
  
  # fit model 
  model = XGBClassifier(**params)
  model.fit(X_train, y_train)
  
  valid_pred = model.predict_proba(X_test)
  
  print(valid_pred) 
  
  #My output looks like - 
  #
  #array([[0.39044815, 0.6095518 ],
  #       [0.4008397 , 0.59916025],
  #       [0.40074524, 0.5992548 ],
  #       ...,
  #       [0.3613969 , 0.6386031 ],
  #       [0.45495912, 0.5450409 ],
  #       [0.41036654, 0.58963346]], dtype=float32)
  #
  #It's give me the 1 or 0 value which I don't want. I want only the max probability. Like 0.6095518,0.59916025...etc.How to do this things?
  
  best_valid_preds = [np.argmax(x) for x in valid_pred]
  print(best_valid_preds)
  </code></pre>