2557d81b3b
Link for line 26 was wrong, it pointed out again for the last demo. I was reading the readme and found the subtle inconsistence. Please, accept this minor change. It works correctly now.
52 lines
2.3 KiB
Markdown
52 lines
2.3 KiB
Markdown
XGBoost Code Examples
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=====================
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This folder contains all the code examples using xgboost.
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* Contribution of examples, benchmarks is more than welcome!
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* If you like to share how you use xgboost to solve your problem, send a pull request:)
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Features Walkthrough
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--------------------
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This is a list of short codes introducing different functionalities of xgboost packages.
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* Basic walkthrough of packages
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[python](guide-python/basic_walkthrough.py)
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[R](../R-package/demo/basic_walkthrough.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
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* Customize loss function, and evaluation metric
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[python](guide-python/custom_objective.py)
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[R](../R-package/demo/custom_objective.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
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* Boosting from existing prediction
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[python](guide-python/boost_from_prediction.py)
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[R](../R-package/demo/boost_from_prediction.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
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* Predicting using first n trees
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[python](guide-python/predict_first_ntree.py)
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[R](../R-package/demo/predict_first_ntree.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/predict_first_ntree.jl)
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* Generalized Linear Model
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[python](guide-python/generalized_linear_model.py)
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[R](../R-package/demo/generalized_linear_model.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)
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* Cross validation
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[python](guide-python/cross_validation.py)
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[R](../R-package/demo/cross_validation.R)
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[Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)
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* Predicting leaf indices
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[python](guide-python/predict_leaf_indices.py)
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[R](../R-package/demo/predict_leaf_indices.R)
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Basic Examples by Tasks
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-----------------------
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Most of examples in this section are based on CLI or python version.
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However, the parameter settings can be applied to all versions
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* [Binary classification](binary_classification)
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* [Multiclass classification](multiclass_classification)
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* [Regression](regression)
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* [Learning to Rank](rank)
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Benchmarks
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----------
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* [Starter script for Kaggle Higgs Boson](kaggle-higgs)
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* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
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