ad68865d6b
* Clean up logic for converting tree_method to updater sequence * Use C++11 enum class for extra safety Compiler will give warnings if switch statements don't handle all possible values of C++11 enum class. Also allow enum class to be used as DMLC parameter. * Fix compiler error + lint * Address reviewer comment * Better docstring for DECLARE_FIELD_ENUM_CLASS * Fix lint * Add C++ test to see if tree_method is recognized * Fix clang-tidy error * Add test_learner.h to R package * Update comments * Fix lint error
eXtreme Gradient Boosting
Community | Documentation | Resources | Contributors | Release Notes
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.