xgboost4j-osx-minimal

H2O XGBoost Libraries

License	The Apache Software License, Version 2.0
Categories	H2 Data Databases H2O Business Logic Libraries Machine Learning
GroupId	ai.h2o
ArtifactId	xgboost4j-osx-minimal
Last Version	1.2.0.16
Release Date	Mar 9, 2021
Type	jar
Description	xgboost4j-osx-minimal H2O XGBoost Libraries
Project URL	https://github.com/h2oai/xgboost
Project Organization	H2O.ai
Source Code Management	https://github.com/h2oai/xgboost

Download xgboost4j-osx-minimal

Filename	Size
xgboost4j-osx-minimal-1.2.0.16.pom
xgboost4j-osx-minimal-1.2.0.16.jar	885 KB
xgboost4j-osx-minimal-1.2.0.16-sources.jar	885 KB
xgboost4j-osx-minimal-1.2.0.16-javadoc.jar	23 KB

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/ai.h2o/xgboost4j-osx-minimal/ -->
<dependency>
    <groupId>ai.h2o</groupId>
    <artifactId>xgboost4j-osx-minimal</artifactId>
    <version>1.2.0.16</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/ai.h2o/xgboost4j-osx-minimal/
implementation 'ai.h2o:xgboost4j-osx-minimal:1.2.0.16'

Gradle Kotlin

// https://jarcasting.com/artifacts/ai.h2o/xgboost4j-osx-minimal/
implementation ("ai.h2o:xgboost4j-osx-minimal:1.2.0.16")

Apache Buildr

'ai.h2o:xgboost4j-osx-minimal:jar:1.2.0.16'

Apache Ivy

<dependency org="ai.h2o" name="xgboost4j-osx-minimal" rev="1.2.0.16">
  <artifact name="xgboost4j-osx-minimal" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='ai.h2o', module='xgboost4j-osx-minimal', version='1.2.0.16')
)

Scala SBT

libraryDependencies += "ai.h2o" % "xgboost4j-osx-minimal" % "1.2.0.16"

Leiningen

[ai.h2o/xgboost4j-osx-minimal "1.2.0.16"]

Dependencies

There are no dependencies for this project. It is a standalone project that does not depend on any other jars.

Project Modules

There are no modules declared in this project.

eXtreme Gradient Boosting

Documentation | Resources | Installation | Release Notes | RoadMap

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.

What's New

• XGBoost GPU support with fast histogram algorithm
• XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
• Story and Lessons Behind the Evolution of XGBoost
• Tutorial: Distributed XGBoost on AWS with YARN
• XGBoost brick Release

Ask a Question

• For reporting bugs please use the xgboost/issues page.
• For generic questions or to share your experience using XGBoost please use the XGBoost User Group

Help to Make XGBoost Better

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.

• Check out call for contributions and Roadmap to see what can be improved, or open an issue if you want something.
• Contribute to the documents and examples to share your experience with other users.
• Add your stories and experience to Awesome XGBoost.
• Please add your name to CONTRIBUTORS.md and after your patch has been merged.
  • Please also update NEWS.md on changes and improvements in API and docs.

License

© Contributors, 2016. Licensed under an Apache-2 license.

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, see also the Project Page at UW.

H2O.ai

Fast Scalable Machine Learning For Smarter Applications

Versions

Version
1.2.0.16 Mar 9, 2021
1.2.0.15 Dec 1, 2020
1.2.0.11 Oct 27, 2020
1.2.0.7 Oct 22, 2020
1.2.0.6 Oct 7, 2020
1.2.0.5 Oct 2, 2020
1.0.0.11 Feb 27, 2020
1.0.0.9 Feb 27, 2020
0.90.6 Dec 11, 2019
0.90.5 Sep 13, 2019
0.90.3 Sep 5, 2019
0.83.17 Apr 11, 2019
0.82.21 Jun 14, 2019
0.82.20 May 21, 2019
0.82.19 May 16, 2019
0.82.18 Apr 24, 2019
0.7.15 Apr 3, 2019
0.7.13 Sep 12, 2018
0.7.12 Sep 11, 2018
0.7.9 Jul 8, 2018
0.7.8 May 25, 2018
0.7.7 May 10, 2018
0.7.6 Apr 25, 2018
0.7.4 Apr 9, 2018
0.7.3 Mar 10, 2018
0.7.2 Mar 9, 2018