d667ea9335
- Install wget explicitly to match openssl. - Install CMake explicitly. - Use newer miniconda link. - Reenable unittests. - gcc@9 + xcode@10 for osx due to missing <_stdio.h>. Other versions of gcc should also work. But as homebrew pour gcc@9 after update by default, so I just stick with latest version. - Disabled one external memory test for OSX. Not sure about the thread implementation in there and fixing external memory is beyond the scope of this PR. - Use Python3 with conda in jvm package.
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.
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
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Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
