- Using XGBoost in Python
- Using XGBoost in R
- Learning to use xgboost by example
- External Memory Version
- Text input format
- Build Instruction
- Notes on the Code
- List of all parameters and their usage: Parameters
- Learning about the model: Introduction to Boosted Trees
- Try to read the binary classification example for getting started example
- Find the guide specific language guide above for the language you like to use
- Learning to use xgboost by example contains lots of useful examples
This section is about blogposts, presentation and videos discussing how to use xgboost to solve your interesting problem. If you think something belongs to here, send a pull request.
- Kaggle CrowdFlower winner's solution by Chenglong Chen
- Kaggle Malware Prediction winner's solution
- Kaggle Tradeshift winning solution by daxiongshu
- Feature Importance Analysis with XGBoost in Tax audit
- Video tutorial: Better Optimization with Repeated Cross Validation and the XGBoost model
- Winning solution of Kaggle Higgs competition: what a single model can do
Contribution of documents and use-cases are welcomed!
- This package use Google C++ style
- Check tool of codestyle
- clone https://github.com/dmlc/dmlc-core into root directory
- type
make lintand fix possible errors.