989ddd036f
* fixed some endian issues * Use dmlc::ByteSwap() to simplify code * Fix lint check * [CI] Add test for s390x * Download latest CMake on s390x * Fix a bug in my code * Save magic number in dmatrix with byteswap on big-endian machine * Save version in binary with byteswap on big-endian machine * Load scalar with byteswap in MetaInfo * Add a debugging message * Handle arrays correctly when byteswapping * EOF can also be 255 * Handle magic number in MetaInfo carefully * Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model * Handle missing packages in Python tests * Don't use boto3 in model compatibility tests * Add s390 Docker file for local testing * Add model compatibility tests * Add R compatibility test * Revert "Add R compatibility test" This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8. Co-authored-by: Qi Zhang <q.zhang@ibm.com> Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
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, 2019. 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.
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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).
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The sponsors in this list are donating cloud hours in lieu of cash donation.
