001503186c
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing. The rewrite has many benefits: - Support for both `max_leaves` and `max_depth`. - Support for `grow_policy`. - Support for mono constraint. - Support for feature weights. - Support for easier bin configuration (`max_bin`). - Support for categorical data. - Faster performance for most of the datasets. (many times faster) - Support for prediction cache. - Significantly better performance for external memory. - Unites the code base between approx and hist.
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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
License
© Contributors, 2021. 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.
Sponsors
Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
Open Source Collective sponsors
Sponsors
Backers
Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
