42 lines
1.0 KiB
Markdown
42 lines
1.0 KiB
Markdown
Learning to rank
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====
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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.
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### Parameters
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The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
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```
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...
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objective="rank:pairwise"
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...
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```
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For more usage details please refer to the [binary classification demo](../binary_classification),
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Instructions
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====
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The dataset for ranking demo is from LETOR04 MQ2008 fold1.
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Before running the examples, you need to get the data by running:
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```
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./wgetdata.sh
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```
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### Command Line
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Run the example:
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```
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./runexp.sh
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```
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### Python
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There are two ways of doing ranking in python.
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Run the example using `xgboost.train`:
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```
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python rank.py
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```
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Run the example using `XGBRanker`:
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```
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python rank_sklearn.py
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```
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