Update broken links (#3565)

Fix #3559
Fix #3562

demo/rank/README.md

@@ -14,8 +14,15 @@ For more usage details please refer to the [binary classification demo](../binar
 
 Instructions
 ====
-The dataset for ranking demo is from LETOR04 MQ2008 fold1,
-You can use the following command to run the example
+The dataset for ranking demo is from LETOR04 MQ2008 fold1.
+You can use the following command to run the example:
 
-Get the data: ./wgetdata.sh
-Run the example: ./runexp.sh
+Get the data:
+```
+./wgetdata.sh
+```
+
+Run the example:
+```
+./runexp.sh
+```

demo/rank/wgetdata.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
-wget http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2008.rar
+wget https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.rar
 unrar x MQ2008.rar
 mv -f MQ2008/Fold1/*.txt .

doc/parameter.rst

@@ -281,7 +281,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
     - ``error``: Binary classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
     - ``error@t``: a different than 0.5 binary classification threshold value could be specified by providing a numerical value through 't'.
     - ``merror``: Multiclass classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``.
-    - ``mlogloss``: `Multiclass logloss <https://www.kaggle.com/wiki/LogLoss>`_.
+    - ``mlogloss``: `Multiclass logloss <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html>`_.
     - ``auc``: `Area under the curve <http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_curve>`_
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
     - ``map``: `Mean average precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_