4109818b32
* add back train method but mark as deprecated * add back train method but mark as deprecated * fix scalastyle error * fix scalastyle error * add back libsvm notes
30 lines
1.4 KiB
Markdown
30 lines
1.4 KiB
Markdown
# XGBoost4J: Distributed XGBoost for Scala/Java
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[](https://travis-ci.org/dmlc/xgboost)
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[](https://xgboost.readthedocs.org/en/latest/jvm/index.html)
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[](../LICENSE)
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[Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) |
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[Resources](../demo/README.md) |
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[Release Notes](../NEWS.md)
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XGBoost4J is the JVM package of xgboost. It brings all the optimizations
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and power xgboost into JVM ecosystem.
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- Train XGBoost models in scala and java with easy customizations.
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- Run distributed xgboost natively on jvm frameworks such as
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Apache Flink and Apache Spark.
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You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) and [Resource Page](../demo/README.md).
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## Examples
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Full code examples for Scala, Java, Apache Spark, and Apache Flink can
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be found in the [examples package](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example).
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**NOTE on LIBSVM Format**:
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* Use *1-based* ascending indexes for the LIBSVM format in distributed training mode
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* Spark does the internal conversion, and does not accept formats that are 0-based
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* Whereas, use *0-based* indexes format when predicting in normal mode - for instance, while using the saved model in the Python package |