XGBoost JVM Package

JVM Package for XGBoost

License	License The Apache License, Version 2.0
GroupId	GroupId ai.rapids
ArtifactId	ArtifactId xgboost-jvm
Last Version	Last Version 0.90.1-Beta
Release Date	Release Date Jul 12, 2019
Type	Type pom
Description	Description XGBoost JVM Package JVM Package for XGBoost
Project URL	Project URL https://github.com/dmlc/xgboost/tree/master/jvm-packages
Source Code Management	Source Code Management https://github.com/rapidsai/xgboost

Download xgboost-jvm

Filename	Size
xgboost-jvm-0.90.1-Beta.pom	19 KB
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How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/ai.rapids/xgboost-jvm/ -->
<dependency>
    <groupId>ai.rapids</groupId>
    <artifactId>xgboost-jvm</artifactId>
    <version>0.90.1-Beta</version>
    <type>pom</type>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/ai.rapids/xgboost-jvm/
implementation 'ai.rapids:xgboost-jvm:0.90.1-Beta'

Gradle Kotlin

// https://jarcasting.com/artifacts/ai.rapids/xgboost-jvm/
implementation ("ai.rapids:xgboost-jvm:0.90.1-Beta")

Apache Buildr

'ai.rapids:xgboost-jvm:pom:0.90.1-Beta'

Apache Ivy

<dependency org="ai.rapids" name="xgboost-jvm" rev="0.90.1-Beta">
  <artifact name="xgboost-jvm" type="pom" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='ai.rapids', module='xgboost-jvm', version='0.90.1-Beta')
)

Scala SBT

libraryDependencies += "ai.rapids" % "xgboost-jvm" % "0.90.1-Beta"

Leiningen

[ai.rapids/xgboost-jvm "0.90.1-Beta"]

Dependencies

compile (5)

Group / Artifact	Type	Version
com.esotericsoftware.kryo : kryo	jar	2.22
org.scala-lang : scala-compiler	jar	2.11.12
org.scala-lang : scala-reflect	jar	2.11.12
org.scala-lang : scala-library	jar	2.11.12
commons-logging : commons-logging	jar	1.2

test (1)

Group / Artifact	Type	Version
org.scalatest : scalatest_2.11	jar	3.0.0

Project Modules

xgboost4j
xgboost4j-spark

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

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.