diff --git a/demo/rank/README.md b/demo/rank/README.md
index 706f09cd4..1f112b4cb 100644
--- a/demo/rank/README.md
+++ b/demo/rank/README.md
@@ -1,6 +1,6 @@
 Learning to rank
 ====
-XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/tutorials/input_format.rst#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank, this function is not yet completed. Currently, we provide pairwise rank.
+XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the [group information file](../../doc/tutorials/input_format.rst#group-input-format) to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank. See [parameters](../../doc/parameter.rst) for supported metrics.
 
 ### Parameters
 The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
@@ -28,7 +28,7 @@ Run the example:
 ```
 
 ### Python
-There are two ways of doing ranking in python.  
+There are two ways of doing ranking in python.
 
 Run the example using `xgboost.train`:
 ```
diff --git a/demo/rank/rank.py b/demo/rank/rank.py
index 2bc260574..d19b2c528 100644
--- a/demo/rank/rank.py
+++ b/demo/rank/rank.py
@@ -34,8 +34,8 @@ test_dmatrix = DMatrix(x_test)
 train_dmatrix.set_group(group_train)
 valid_dmatrix.set_group(group_valid)
 
-params = {'objective': 'rank:pairwise', 'eta': 0.1, 'gamma': 1.0,
-               'min_child_weight': 0.1, 'max_depth': 6}
+params = {'objective': 'rank:ndcg', 'eta': 0.1, 'gamma': 1.0,
+          'min_child_weight': 0.1, 'max_depth': 6}
 xgb_model = xgb.train(params, train_dmatrix, num_boost_round=4,
-                           evals=[(valid_dmatrix, 'validation')])
+                      evals=[(valid_dmatrix, 'validation')])
 pred = xgb_model.predict(test_dmatrix)
diff --git a/demo/rank/rank_sklearn.py b/demo/rank/rank_sklearn.py
index 1d8341d17..723b8c7d9 100644
--- a/demo/rank/rank_sklearn.py
+++ b/demo/rank/rank_sklearn.py
@@ -2,7 +2,6 @@
 import xgboost as xgb
 from sklearn.datasets import load_svmlight_file
 
-
 #  This script demonstrate how to do ranking with XGBRanker
 x_train, y_train = load_svmlight_file("mq2008.train")
 x_valid, y_valid = load_svmlight_file("mq2008.vali")
@@ -26,10 +25,10 @@ with open("mq2008.test.group", "r") as f:
     for line in data:
         group_test.append(int(line.split("\n")[0]))
 
-params = {'objective': 'rank:pairwise', 'learning_rate': 0.1,
+params = {'objective': 'rank:ndcg', 'learning_rate': 0.1,
           'gamma': 1.0, 'min_child_weight': 0.1,
           'max_depth': 6, 'n_estimators': 4}
 model = xgb.sklearn.XGBRanker(**params)
-model.fit(x_train, y_train, group_train,
+model.fit(x_train, y_train, group_train, verbose=True,
           eval_set=[(x_valid, y_valid)], eval_group=[group_valid])
 pred = model.predict(x_test)