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Update broken links (#3565)

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Fix #3559
Fix #3562
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Philip Hyunsu Cho 2018-08-07 05:27:39 -07:00 committed by GitHub
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15
demo/rank/README.md
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@ -14,8 +14,15 @@ For more usage details please refer to the [binary classification demo](../binar
Instructions Instructions
==== ====
The dataset for ranking demo is from LETOR04 MQ2008 fold1, The dataset for ranking demo is from LETOR04 MQ2008 fold1.
You can use the following command to run the example You can use the following command to run the example:
Get the data: ./wgetdata.sh Get the data:
Run the example: ./runexp.sh ```
./wgetdata.sh
```
Run the example:
```
./runexp.sh
```

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demo/rank/wgetdata.sh
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@ -1,4 +1,4 @@
#!/bin/bash #!/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 unrar x MQ2008.rar
mv -f MQ2008/Fold1/*.txt . mv -f MQ2008/Fold1/*.txt .

2
doc/parameter.rst
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@ -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``: 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'. - ``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)``. - ``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>`_ - ``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>`_ - ``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>`_ - ``map``: `Mean average precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_