8905df4a18
* Basic script for using compilation database. * Add `GENERATE_COMPILATION_DATABASE' to CMake. * Rearrange CMakeLists.txt. * Add basic python clang-tidy script. * Remove modernize-use-auto. * Add clang-tidy to Jenkins * Refine logic for correct path detection In Jenkins, the project root is of form /home/ubuntu/workspace/xgboost_PR-XXXX * Run clang-tidy in CUDA 9.2 container * Use clang_tidy container
eXtreme Gradient Boosting
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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.