ml.dmlc:xgboost4j

JVM Package for XGBoost

Лицензия	Лицензия The Apache License, Version 2.0
Группа	Группа ml.dmlc
Идентификатор	Идентификатор xgboost4j
Последняя версия	Последняя версия 0.90
Дата	Дата 20 мая 2019 г.
Тип	Тип jar
Описание	Описание JVM Package for XGBoost
Ссылка на сайт	Ссылка на сайт https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j

Скачать xgboost4j

Имя Файла	Размер
xgboost4j-0.90.pom
xgboost4j-0.90.jar	2 MB
xgboost4j-0.90-sources.jar	1 MB
xgboost4j-0.90-javadoc.jar	99 KB
Обзор

Как подключить последнюю версию

Apache Maven

<!-- https://jarcasting.com/artifacts/ml.dmlc/xgboost4j/ -->
<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j</artifactId>
    <version>0.90</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/ml.dmlc/xgboost4j/
implementation 'ml.dmlc:xgboost4j:0.90'

Gradle Kotlin

// https://jarcasting.com/artifacts/ml.dmlc/xgboost4j/
implementation ("ml.dmlc:xgboost4j:0.90")

Apache Buildr

'ml.dmlc:xgboost4j:jar:0.90'

Apache Ivy

<dependency org="ml.dmlc" name="xgboost4j" rev="0.90">
  <artifact name="xgboost4j" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='ml.dmlc', module='xgboost4j', version='0.90')
)

Scala SBT

libraryDependencies += "ml.dmlc" % "xgboost4j" % "0.90"

Leiningen

[ml.dmlc/xgboost4j "0.90"]

Зависимости

compile (6)

Идентификатор библиотеки	Тип	Версия
com.typesafe.akka : akka-actor_2.11	jar	2.3.11
com.esotericsoftware.kryo : kryo	jar	2.21
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 (3)

Идентификатор библиотеки	Тип	Версия
junit : junit	jar	4.11
com.typesafe.akka : akka-testkit_2.11	jar	2.3.11
org.scalatest : scalatest_2.11	jar	3.0.0

Модули Проекта

Данный проект не имеет модулей.

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.