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xgboost

https://github.com/yanqili/xgboost

Science Score: 41.0%

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    Links to: arxiv.org
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Repository

Basic Info
  • Host: GitHub
  • Owner: yanqili
  • License: apache-2.0
  • Language: C++
  • Default Branch: master
  • Size: 32.7 MB
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  • Watchers: 1
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Created over 7 years ago · Last pushed over 7 years ago
Metadata Files
Readme Changelog License Citation

README.md

eXtreme Gradient Boosting

Build Status Build Status Documentation Status GitHub license CRAN Status Badge PyPI version

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 (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

License

© Contributors, 2016. Licensed under an Apache-2 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.

Owner

  • Login: yanqili
  • Kind: user

Citation (CITATION) 

@inproceedings{Chen:2016:XST:2939672.2939785,
 author = {Chen, Tianqi and Guestrin, Carlos},
 title = {{XGBoost}: A Scalable Tree Boosting System},
 booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '16},
 year = {2016},
 isbn = {978-1-4503-4232-2},
 location = {San Francisco, California, USA},
 pages = {785--794},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2939672.2939785},
 doi = {10.1145/2939672.2939785},
 acmid = {2939785},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {large-scale machine learning},
}

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Dependencies

R-package/DESCRIPTION cran
  • R >= 3.3.0 depends
  • Matrix >= 1.1 imports
  • data.table >= 1.9.6 imports
  • magrittr >= 1.5 imports
  • methods * imports
  • stringi >= 0.5.2 imports
  • Ckmeans.1d.dp >= 3.3.1 suggests
  • DiagrammeR >= 0.9.0 suggests
  • ggplot2 >= 1.0.1 suggests
  • igraph >= 1.0.1 suggests
  • knitr * suggests
  • lintr * suggests
  • rmarkdown * suggests
  • testthat * suggests
  • vcd >= 1.3 suggests
jvm-packages/pom.xml maven
  • com.esotericsoftware.kryo:kryo 2.21
  • commons-logging:commons-logging 1.2
  • org.scala-lang:scala-compiler 2.11.8
  • org.scala-lang:scala-library 2.11.8
  • org.scala-lang:scala-reflect 2.11.8
  • org.scalatest:scalatest_2.11 3.0.0 test
jvm-packages/xgboost4j/pom.xml maven
  • com.typesafe.akka:akka-actor_${scala.binary.version} 2.3.11 compile
  • com.typesafe.akka:akka-testkit_${scala.binary.version} 2.3.11 test
  • junit:junit 4.11 test
jvm-packages/xgboost4j-example/pom.xml maven
  • org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
  • ml.dmlc:xgboost4j-flink 0.81-SNAPSHOT
  • ml.dmlc:xgboost4j-spark 0.81-SNAPSHOT
  • org.apache.commons:commons-lang3 3.4
jvm-packages/xgboost4j-flink/pom.xml maven
  • ml.dmlc:xgboost4j 0.81-SNAPSHOT
  • org.apache.commons:commons-lang3 3.4
  • org.apache.flink:flink-clients_${scala.binary.version} ${flink.version}
  • org.apache.flink:flink-ml_${scala.binary.version} ${flink.version}
  • org.apache.flink:flink-scala_${scala.binary.version} ${flink.version}
  • org.apache.hadoop:hadoop-common 2.7.3
jvm-packages/xgboost4j-spark/pom.xml maven
  • org.apache.spark:spark-core_${scala.binary.version} ${spark.version} provided
  • org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
  • org.apache.spark:spark-sql_${scala.binary.version} ${spark.version} provided
  • ml.dmlc:xgboost4j 0.81-SNAPSHOT
doc/requirements.txt pypi
  • breathe *
  • graphviz *
  • guzzle_sphinx_theme *
  • matplotlib >=2.1
  • mock *
  • numpy *
  • sh >=1.12.14
  • sphinx *
python-package/setup.py pypi
  • numpy *
  • scipy *