XGBoost Examples
====
This folder contains all the code examples using xgboost. 

* Contribution of examples, benchmarks is more than welcome!
* If you like to share how you use xgboost to solve your problem, send a pull request:)
 
Features Walkthrough
====
This is a list of short codes introducing different functionalities of xgboost and its wrapper.
* Basic walkthrough of wrappers 
  [python](guide-python/basic_walkthrough.py)
  [R](../R-package/demo/basic_walkthrough.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/basic_walkthrough.jl)
* Customize loss function, and evaluation metric
  [python](guide-python/custom_objective.py)
  [R](../R-package/demo/custom_objective.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/custom_objective.jl)
* Boosting from existing prediction
  [python](guide-python/boost_from_prediction.py)
  [R](../R-package/demo/boost_from_prediction.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)
* Predicting using first n trees 
  [python](guide-python/predict_first_ntree.py)
  [R](../R-package/demo/boost_from_prediction.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/boost_from_prediction.jl)  
* Generalized Linear Model
  [python](guide-python/generalized_linear_model.py)
  [R](../R-package/demo/generalized_linear_model.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/generalized_linear_model.jl)  
* Cross validation
  [python](guide-python/cross_validation.py)
  [R](../R-package/demo/cross_validation.R)
  [Julia](https://github.com/antinucleon/XGBoost.jl/blob/master/demo/cross_validation.jl)  
* Predicting leaf indices
  [python](guide-python/predict_leaf_indices.py)
  [R](../R-package/demo/predict_leaf_indices.R)

Basic Examples by Tasks
====
Most of examples in this section are based on CLI or python version.
However, the parameter settings can be applied to all versions
* [Binary classification](binary_classification)
* [Multiclass classification](multiclass_classification)
* [Regression](regression)
* [Learning to Rank](rank)

Benchmarks
====
* [Starter script for Kaggle Higgs Boson](kaggle-higgs)
* [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)