4ed8a88240
* Add XGBRanker to Python API doc * Show inherited members of XGBRegressor in API doc, since XGBRegressor uses default methods from XGBModel * Add table of contents to Python API doc * Skip JVM doc download if not available * Show inherited members for XGBRegressor and XGBRanker * Expose XGBRanker to Python XGBoost module directory * Add docstring to XGBRegressor.predict() and XGBRanker.predict() * Fix rendering errors in Python docstrings * Fix lint
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.