3c506b076e
* bump scala to 2.12 which requires java 8 and also newer flink and akka * put scala version in artifactId * fix appveyor * fix for scaladoc issue that looks like https://github.com/scala/bug/issues/10509 * fix ci_build * update versions in generate_pom.py * fix generate_pom.py * apache does not have a download for spark 2.4.3 distro using scala 2.12 yet, so for now i use a tgz i put on s3 * Upload spark-2.4.3-bin-scala2.12-hadoop2.7.tgz to our own S3 * Update Dockerfile.jvm_cross * Update Dockerfile.jvm_cross
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.
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
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Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
