title: Cannot use mean absolute error metric (mae) in python XGBoost
body: <p>I'm starting with XGBoost and willing to use it for a regression problem. </p>
  
  <p>My evaluation metric is mean absolute error (mae). As far I understand, we can use XGB with this metric (<a href="https://github.com/dmlc/xgboost/blob/master/doc/parameter.md#learning-task-parameters" rel="noreferrer">https://github.com/dmlc/xgboost/blob/master/doc/parameter.md#learning-task-parameters</a>).</p>
  
  <p>I tried using XGB CV with the parameter <code>metrics = ['mae']</code> but I get an error that says mae metric doesn't exist: <code>XGBoostError: unknown evaluation metric type: mae</code></p>
  
  <p>Here is my code:</p>
  
  <pre><code>xgdmat = xgb.DMatrix(train, y_train)
  
  our_params = {'eta': 0.5,
                'seed': 0,
                'subsample': 1,
                'colsample_bytree': 1, 
                'max_depth': 3,
                'min_child_weight': 3,
                'objective': 'reg:linear'} 
  
  cv_xgb = xgb.cv(params = our_params, dtrain = xgdmat, num_boost_round = 3000, nfold = 5,
                  metrics = ['mae'],
                  early_stopping_rounds = 100)
  </code></pre>
  
  <p>Did anyone already faced this issue and can help me solve this ?</p>
  
  <p>Many thanks.</p>