schwt/xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

changed version by schwt

• new makefile configure, suitable for my spark context. (done by Sean.Zhong)
• new predict function (def my_predict()), which easy to evaluate.

notes:

1. added new function my_predict() from premal predict() in version: java, scala, spark.scala
2. tocheck: in java version, DMatrix.getLabel(), keep order?

eXtreme Gradient Boosting

Documentation | Resources | Installation | Release Notes | RoadMap

NOTES on the build

1. git clone --recursive https://github.com/dmlc/xgboost.git
2. Make sure the gcc version >= 4.8. Make sure g++/gcc can be found on PATH env. Make sure the GCC library can be found in runtime.

   export LD_LIBRARY_PATH=${YOUR_GCC_DIRECTORY}/lib64
   

3. Make sure you use a new verion of ld. I use binutils version 2.2.8. Make sure ld can be found on PATH env.
4. Make sure the cmake version > 3.2. Make sure cmake can be found on PATH env.
5. export JAVA_HOME=/path/to/your/jdk
6. To enable HDFS support, please modify jvm-packages/create_jni.py. "USE_HDFS": "ON" libhdfs.a is not compiled with -fPIC flag, which will cause building problems. We need to recompile libhdfs.a with -fPIC support by following link dmlc/dmlc-core#10 (comment) I have compiled a new libhdfs.a for CDH 5.3.2.

   export HADOOP_HOME=${YOUR_XGBOOST_SRC_DIR}/hadoop-hdfs-2.5.0-cdh5.3.2
   

7. Make the build

   make clean
   make -j4
   cd jvm-packages
   mvn clean
   mvn package
   

8. The build has been verified with gcc 4.8.2, cmake 3.8.2 on a CentOS 6.6 machine.

XGBoost

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.