xgboost/jvm-packages/README.md

XGBoost4J: Distributed XGBoost for Scala/Java

Documentation | Resources | Release Notes

XGBoost4J is the JVM package of xgboost. It brings all the optimizations and power xgboost into JVM ecosystem.

• Train XGBoost models in scala and java with easy customizations.
• Run distributed xgboost natively on jvm frameworks such as Apache Flink and Apache Spark.

You can find more about XGBoost on Documentation and Resource Page.

Add Maven Dependency

XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5

Access release version

maven

<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j</artifactId>
    <version>latest_version_num</version>
</dependency>

sbt

 "ml.dmlc" % "xgboost4j" % "latest_version_num"

For the latest release version number, please check here.

if you want to use xgboost4j-spark, you just need to replace xgboost4j with xgboost4j-spark

Access SNAPSHOT version

You need to add github as repo:

maven:

<repository>
  <id>GitHub Repo</id>
  <name>GitHub Repo</name>
  <url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
</repository>

sbt:

resolvers += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"

the add dependency as following:

maven

<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j</artifactId>
    <version>latest_version_num</version>
</dependency>

sbt

 "ml.dmlc" % "xgboost4j" % "latest_version_num"

For the latest release version number, please check here.

if you want to use xgboost4j-spark, you just need to replace xgboost4j with xgboost4j-spark

Examples

Full code examples for Scala, Java, Apache Spark, and Apache Flink can be found in the examples package.

NOTE on LIBSVM Format:

• Use 1-based ascending indexes for the LIBSVM format in distributed training mode

  • Spark does the internal conversion, and does not accept formats that are 0-based

• Whereas, use 0-based indexes format when predicting in normal mode - for instance, while using the saved model in the Python package