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CLI is not most developed interface. Putting them into correct directory can help new users to avoid it as most of the use cases are from a language binding.
Learning to rank
XGBoost supports accomplishing ranking tasks. In ranking scenario, data are often grouped and we need the group information file to specify ranking tasks. The model used in XGBoost for ranking is the LambdaRank. See parameters for supported metrics.
Parameters
The configuration setting is similar to the regression and binary classification setting, except user need to specify the objectives:
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objective="rank:pairwise"
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For more usage details please refer to the binary classification demo,
Instructions
The dataset for ranking demo is from LETOR04 MQ2008 fold1. Before running the examples, you need to get the data by running:
./wgetdata.sh
Command Line
Run the example:
./runexp.sh
Python
There are two ways of doing ranking in python.
Run the example using xgboost.train:
python rank.py
Run the example using XGBRanker:
python rank_sklearn.py