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xgboost

https://github.com/zhztheplayer/xgboost

Science Score: 38.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    18 of 413 committers (4.4%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.0%) to scientific vocabulary

Keywords from Contributors

gbdt gbm gbrt xgboost distributed flexible alignment deep-neural-networks probabilistic-programming mlops
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: zhztheplayer
  • License: apache-2.0
  • Language: C++
  • Default Branch: hongze-dev
  • Size: 13 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Created over 6 years ago · Last pushed about 4 years ago
Metadata Files
Readme License Citation

README.md

eXtreme Gradient Boosting

Build Status 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 (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.

License

© Contributors, 2019. 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.

Sponsors

Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).

Open Source Collective sponsors

Backers on Open Collective Sponsors on Open Collective

Sponsors

[Become a sponsor]

NVIDIA

Backers

[Become a backer]

Other sponsors

The sponsors in this list are donating cloud hours in lieu of cash donation.

Amazon Web Services

Owner

  • Name: Hongze Zhang
  • Login: zhztheplayer
  • 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},
}

GitHub Events

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Last synced: 11 months ago

All Time
  • Total Commits: 3,387
  • Total Committers: 413
  • Avg Commits per committer: 8.201
  • Development Distribution Score (DDS): 0.701
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Top Committers
Name Email Commits
tqchen t****n@g****m 1,013
El Potaeto p****e@m****m 252
Tong He h****7@g****m 206
Philip Hyunsu Cho c****1@c****u 186
Nan Zhu C****t 160
Jiaming Yuan j****n@o****m 138
Rory Mitchell r****z@g****m 104
Vadim Khotilovich k****h@g****m 98
terrytangyuan t****n@g****m 97
Hongze Zhang h****g@i****m 71
giuliohome g****e@g****m 40
Rong Ou r****u@g****m 37
kalenhaha c****2@g****m 34
tqchen@graphlab.com t****n@g****m 32
Faron f****z@g****m 30
AbdealiJK a****i@g****m 27
Sergei Lebedev s****y@g****m 26
Skipper Seabold j****d@g****m 23
pommedeterresautee S****3 23
Johan Manders j****n@s****m 21
Ajinkya Kale k****a@g****m 20
sinhrks s****s@g****m 20
Andy Adinets a****z@g****m 20
antinucleon a****n@g****m 19
phunterlau p****u@g****m 18
Boliang Chen c****u@g****m 15
yanqingmen t****y@g****m 14
sriramch 3****h 13
Xu Xiao l****f@g****m 10
Yun Ni E****1@g****m 10
and 383 more...
Committer Domains (Top 20 + Academic)
qq.com: 8 163.com: 5 foxmail.com: 4 cs.washington.edu: 2 intel.com: 2 126.com: 2 ya.ru: 2 mail.ru: 2 me.com: 2 allstate.com: 1 binghamton.edu: 1 bieniek.org.pl: 1 sina.com: 1 dfscholars.org: 1 apache.org: 1 uber.com: 1 p5w.net: 1 raysbaseball.com: 1 returnpath.com: 1 cos.name: 1 case.edu: 1 umail.iu.edu: 1 pku.edu.cn: 1 humboldt.edu: 1 alumni.stanford.edu: 1 cs.cmu.edu: 1 andrew.cmu.edu: 1 noaa.gov: 1 essec.edu: 1 duke.edu: 1 ischool.berkeley.edu: 1 post.harvard.edu: 1 alum.utoronto.ca: 1 ntu.edu.tw: 1 mail.hfut.edu.cn: 1

Issues and Pull Requests

Last synced: 11 months ago

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  • Total issues: 0
  • Total pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 months
  • Total issue authors: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0.33
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dependencies (3)

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
  • float * suggests
  • ggplot2 >= 1.0.1 suggests
  • igraph >= 1.0.1 suggests
  • jsonlite * suggests
  • knitr * suggests
  • lintr * suggests
  • rmarkdown * suggests
  • testthat * suggests
  • vcd >= 1.3 suggests
jvm-packages/pom.xml maven
  • com.esotericsoftware.kryo:kryo 2.22
  • commons-logging:commons-logging 1.2
  • org.scala-lang:scala-compiler 2.12.10
  • org.scala-lang:scala-library 2.12.10
  • org.scala-lang:scala-reflect 2.12.10
  • org.scalactic:scalactic_2.12 3.0.8 test
  • org.scalatest:scalatest_2.12 3.0.8 test
jvm-packages/xgboost4j/pom.xml maven
  • com.typesafe.akka:akka-actor_${scala.binary.version} 2.5.23 compile
  • com.typesafe.akka:akka-testkit_${scala.binary.version} 2.5.23 test
  • junit:junit 4.11 test
jvm-packages/xgboost4j-example/pom.xml maven
  • org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
  • ml.dmlc:xgboost4j-flink_${scala.binary.version} 1.0.0-SNAPSHOT
  • ml.dmlc:xgboost4j-spark_${scala.binary.version} 1.0.0-SNAPSHOT
  • org.apache.commons:commons-lang3 3.4
jvm-packages/xgboost4j-flink/pom.xml maven
  • ml.dmlc:xgboost4j_${scala.binary.version} 1.0.0-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.arrow:arrow-vector ${arrow.version} provided
  • org.apache.spark:spark-core_${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} ${spark.version} provided
  • ml.dmlc:xgboost4j_${scala.binary.version} 1.0.0-SNAPSHOT
doc/requirements.txt pypi
  • breathe *
  • graphviz *
  • guzzle_sphinx_theme *
  • matplotlib >=2.1
  • mock *
  • numpy *
  • sh >=1.12.14
  • sphinx >=2.1
python-package/setup.py pypi
  • numpy *
  • scipy *