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https://github.com/dmlc/xgboost

distributed-systems gbdt gbm gbrt machine-learning xgboost

Score: 35.33289535227183

Last synced: about 23 hours ago
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Repository metadata:

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, Dask, Flink and DataFlow

Owner metadata:

GitHub Events

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Last Year

Committers metadata

Last synced: 2 days ago

Total Commits: 7,473
Total Committers: 723
Avg Commits per committer: 10.336
Development Distribution Score (DDS): 0.729

Commits in past year: 565
Committers in past year: 76
Avg Commits per committer in past year: 7.434
Development Distribution Score (DDS) in past year: 0.604

Name Email Commits
Jiaming Yuan j****n@o****m 2024
tqchen t****n@g****m 1404
Philip Hyunsu Cho c****1@c****u 560
Rory Mitchell r****z@g****m 313
El Potaeto p****e@m****m 252
Tong He h****7@g****m 207
dependabot[bot] 4****] 178
Nan Zhu C****t 168
Rong Ou r****u@g****m 131
Bobby Wang w****8@g****m 126
david-cortes d****a@g****m 126
terrytangyuan t****n@g****m 108
Vadim Khotilovich k****h@g****m 98
James Lamb j****0@g****m 85
Dmitry Razdoburdin d****n@g****m 61
nachocano n****o@g****m 48
giuliohome g****e@g****m 41
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 28
Sergei Lebedev s****y@g****m 26
Boliang Chen c****u@g****m 26
Andy Adinets a****z@g****m 24
pommedeterresautee S****3 23
Skipper Seabold j****d@g****m 23
ShvetsKS 3****S 22
Johan Manders j****n@s****m 21
sinhrks s****s@g****m 20
Ajinkya Kale k****a@g****m 20
and 693 more...

Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 5,191
Total pull requests: 7,627
Average time to close issues: 5 months
Average time to close pull requests: 22 days
Total issue authors: 2,941
Total pull request authors: 842
Average comments per issue: 4.42
Average comments per pull request: 1.98
Merged pull request: 5,917
Bot issues: 3
Bot pull requests: 827

Past year issues: 176
Past year pull requests: 803
Past year average time to close issues: 21 days
Past year average time to close pull requests: 7 days
Past year issue authors: 88
Past year pull request authors: 140
Past year average comments per issue: 3.27
Past year average comments per pull request: 1.22
Past year merged pull request: 608
Past year bot issues: 0
Past year bot pull requests: 29

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/dmlc/xgboost

Top Issue Authors

  • trivialfis (356)
  • chyunsu3 (216)
  • pseudotensor (89)
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  • RAMitchell (54)
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  • ghost (33)
  • KOLANICH (29)
  • wbo4958 (26)
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  • mayer79 (14)

Top Pull Request Authors

  • trivialfis (3,025)
  • dependabot[bot] (813)
  • chyunsu3 (650)
  • RAMitchell (344)
  • david-cortes (210)
  • wbo4958 (190)
  • CodingCat (149)
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  • razdoburdin (113)
  • hcho3 (109)
  • jameslamb (92)
  • khotilov (43)
  • Nityahapani (41)
  • superbobry (34)
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Top Issue Labels

  • feature-request (339)
  • status: need update (271)
  • type: bug (210)
  • type: roadmap (61)
  • CI (44)
  • doc (44)
  • Blocking (39)
  • type: java-scala (36)
  • type: question (35)
  • status: help wanted (33)
  • status: RFC (28)
  • cross-validation (20)
  • LTR (19)
  • dask (16)
  • type: r-package (12)
  • good first issue (12)
  • known-issue (11)
  • type: python (9)
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  • ? Triage (5)
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  • dependencies (3)
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  • priority: low (2)
  • type: installation (1)
  • priority: medium (1)
  • stale (1)
  • A: In progress (1)

Top Pull Request Labels

  • dependencies (818)
  • java (242)
  • Blocking (161)
  • github_actions (79)
  • stale (62)
  • status: need review (21)
  • status: WIP (18)
  • type: python (3)
  • CI (3)
  • type: bug (3)
  • type: r-package (2)
  • feature-request (1)
  • spam (1)
  • dask (1)
  • Breaking (1)

Package metadata

nixpkgs-23.11: xgboostWithCuda

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-unstable: python313Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

pypi.org: xgboost

XGBoost Python Package

  • Homepage:
  • Documentation: https://xgboost.readthedocs.io/
  • Licenses: Apache-2.0
  • Latest release: 3.4.1 (published 3 months ago)
  • Last Synced: 2026-11-01T12:41:13.656Z (1 day ago)
  • Versions: 98
  • Dependent Packages: 684
  • Dependent Repositories: 12,601
  • Downloads: 29,704,813 Last month
  • Docker Downloads: 25,981,324
  • Rankings:
    • Dependent packages count: 0.036%
    • Downloads: 0.076%
    • Dependent repos count: 0.076%
    • Forks count: 0.107%
    • Average: 0.187%
    • Stargazers count: 0.203%
    • Docker downloads count: 0.621%
  • Maintainers (3)
nixpkgs-unstable: xgboostWithCuda

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

conda-forge.org: 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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-07-26T03:00:43.988Z (3 months ago)
  • Versions: 24
  • Dependent Packages: 23
  • Dependent Repositories: 221
  • Downloads: 7,239,424 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 1.887%
    • Dependent repos count: 2.251%
    • Dependent packages count: 2.9%
repo1.maven.org: ml.dmlc:xgboost4j

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 0.90 (published over 7 years ago)
  • Last Synced: 2026-10-31T16:06:27.549Z (2 days ago)
  • Versions: 5
  • Dependent Packages: 17
  • Dependent Repositories: 62
  • Docker Downloads: 31,458
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 2.206%
    • Dependent repos count: 2.637%
    • Docker downloads count: 3.057%
    • Dependent packages count: 3.654%
repo1.maven.org: ml.dmlc:xgboost4j_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:33.662Z (2 days ago)
  • Versions: 33
  • Dependent Packages: 19
  • Dependent Repositories: 23
  • Docker Downloads: 7,440
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 2.474%
    • Dependent packages count: 3.28%
    • Dependent repos count: 4.933%
conda-forge.org: py-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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T16:17:34.594Z (7 months ago)
  • Versions: 18
  • Dependent Packages: 13
  • Dependent Repositories: 122
  • Downloads: 12,044,130 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 2.573%
    • Dependent repos count: 3.08%
    • Dependent packages count: 4.817%
repo1.maven.org: ml.dmlc:xgboost4j-spark

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 0.90 (published over 7 years ago)
  • Last Synced: 2026-10-31T16:06:32.043Z (2 days ago)
  • Versions: 5
  • Dependent Packages: 9
  • Dependent Repositories: 64
  • Docker Downloads: 30,184
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Dependent repos count: 2.584%
    • Average: 2.781%
    • Docker downloads count: 3.057%
    • Dependent packages count: 6.581%
proxy.golang.org: github.com/dmlc/xgboost

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/dmlc/xgboost#section-documentation
  • Licenses: apache-2.0
  • Latest release: v3.4.2+incompatible (published about 2 months ago)
  • Last Synced: 2026-10-31T16:06:41.932Z (2 days ago)
  • Versions: 53
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.025%
    • Stargazers count: 0.069%
    • Average: 3.318%
    • Dependent repos count: 4.802%
    • Dependent packages count: 8.376%
cran.r-project.org: xgboost

Extreme Gradient Boosting

  • Homepage: https://github.com/dmlc/xgboost
  • Documentation: http://cran.r-project.org/web/packages/xgboost/xgboost.pdf
  • Licenses: Apache License (== 2.0) | file LICENSE
  • Latest release: 0.82.1 (published over 7 years ago)
  • Last Synced: 2026-10-31T16:06:42.349Z (2 days ago)
  • Versions: 42
  • Dependent Packages: 145
  • Dependent Repositories: 520
  • Downloads: 69,408 Last month
  • Docker Downloads: 119,919
  • Rankings:
    • Stargazers count: 0.0%
    • Forks count: 0.0%
    • Dependent repos count: 0.644%
    • Dependent packages count: 0.719%
    • Downloads: 1.354%
    • Average: 3.676%
    • Docker downloads count: 19.339%
  • Maintainers (1)
repo1.maven.org: ml.dmlc:xgboost4j-spark_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:07:02.421Z (2 days ago)
  • Versions: 33
  • Dependent Packages: 11
  • Dependent Repositories: 12
  • Docker Downloads: 7,031
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 3.943%
    • Dependent packages count: 5.472%
    • Docker downloads count: 5.566%
    • Dependent repos count: 6.992%
repo1.maven.org: ml.dmlc:xgboost4j_2.11

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j_2.11/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.1.2 (published almost 6 years ago)
  • Last Synced: 2026-10-31T16:06:59.785Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 21
  • Dependent Repositories: 2
  • Docker Downloads: 66,596
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Docker downloads count: 2.714%
    • Dependent packages count: 2.995%
    • Average: 4.681%
    • Dependent repos count: 16.014%
repo1.maven.org: ml.dmlc:xgboost4j-spark_2.11

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark_2.11/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.1.2 (published almost 6 years ago)
  • Last Synced: 2026-10-31T16:06:38.142Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 16
  • Dependent Repositories: 2
  • Docker Downloads: 66,596
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Docker downloads count: 2.714%
    • Dependent packages count: 3.856%
    • Average: 4.854%
    • Dependent repos count: 16.014%
conda-forge.org: libxgboost

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-03-18T17:07:55.840Z (8 months ago)
  • Versions: 18
  • Dependent Packages: 4
  • Dependent Repositories: 37
  • Downloads: 10,914,248 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 5.2%
    • Dependent repos count: 5.915%
    • Dependent packages count: 12.488%
conda-forge.org: _py-xgboost-mutex

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 2.0 (published about 8 years ago)
  • Last Synced: 2026-04-01T16:15:59.406Z (7 months ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 36
  • Downloads: 9,137,656 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Dependent repos count: 6.039%
    • Average: 7.004%
    • Dependent packages count: 19.581%
conda-forge.org: r-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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T16:17:10.821Z (7 months ago)
  • Versions: 18
  • Dependent Packages: 7
  • Dependent Repositories: 3
  • Downloads: 1,178,108 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 7.08%
    • Dependent packages count: 8.011%
    • Dependent repos count: 17.914%
anaconda.org: py-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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 3.3.0 (published 4 months ago)
  • Last Synced: 2026-07-08T19:06:02.580Z (4 months ago)
  • Versions: 17
  • Dependent Packages: 2
  • Dependent Repositories: 122
  • Downloads: 1,020,254 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 9.299%
    • Dependent packages count: 13.462%
    • Dependent repos count: 17.326%
repo1.maven.org: ml.dmlc:xgboost4j-flink

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-flink/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 0.90 (published over 7 years ago)
  • Last Synced: 2026-10-31T16:06:44.437Z (2 days ago)
  • Versions: 5
  • Dependent Packages: 1
  • Dependent Repositories: 19
  • Docker Downloads: 2,911
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Dependent repos count: 5.498%
    • Average: 9.977%
    • Dependent packages count: 32.725%
anaconda.org: libxgboost

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 3.3.0 (published 4 months ago)
  • Last Synced: 2026-07-08T19:05:45.982Z (4 months ago)
  • Versions: 17
  • Dependent Packages: 4
  • Dependent Repositories: 37
  • Downloads: 1,023,866 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 6.992%
    • Average: 10.028%
    • Dependent repos count: 26.71%
anaconda.org: 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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 3.3.0 (published 4 months ago)
  • Last Synced: 2026-07-08T19:06:32.187Z (4 months ago)
  • Versions: 12
  • Dependent Packages: 3
  • Dependent Repositories: 221
  • Downloads: 37,624 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 10.242%
    • Dependent repos count: 13.033%
    • Dependent packages count: 21.527%
conda-forge.org: _r-xgboost-mutex

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 2.0 (published about 8 years ago)
  • Last Synced: 2026-04-01T16:15:55.176Z (7 months ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Downloads: 140,957 Total
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 11.52%
    • Dependent packages count: 19.581%
    • Dependent repos count: 24.103%
repo1.maven.org: ml.dmlc:xgboost4j-gpu_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-gpu_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 2.1.4 (published over 1 year ago)
  • Last Synced: 2026-10-31T16:06:50.117Z (2 days ago)
  • Versions: 21
  • Dependent Packages: 1
  • Dependent Repositories: 4
  • Docker Downloads: 162
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 11.614%
    • Dependent repos count: 12.047%
    • Dependent packages count: 32.725%
anaconda.org: _py-xgboost-mutex

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.0 (published over 8 years ago)
  • Last Synced: 2026-03-18T17:07:45.941Z (8 months ago)
  • Versions: 2
  • Dependent Packages: 2
  • Dependent Repositories: 36
  • Downloads: 1,056,438 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 11.73%
    • Dependent packages count: 13.462%
    • Dependent repos count: 27.047%
repo1.maven.org: com.nvidia:xgboost4j_3.0

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.nvidia/xgboost4j_3.0/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.4.2-0.3.0 (published over 4 years ago)
  • Last Synced: 2026-10-31T16:06:43.507Z (2 days ago)
  • Versions: 7
  • Dependent Packages: 1
  • Dependent Repositories: 2
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 12.606%
    • Dependent repos count: 16.014%
    • Dependent packages count: 32.725%
nuget.org: libxgboost-2.0.3-osx-x64

A package containing the libxgboost native library for osx-x64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.1 (published over 1 year ago)
  • Last Synced: 2026-10-31T16:06:32.861Z (2 days ago)
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 11,302 Total
  • Rankings:
    • Dependent repos count: 5.722%
    • Average: 13.263%
    • Dependent packages count: 15.296%
    • Downloads: 18.77%
  • Maintainers (1)
repo1.maven.org: com.intel.bigdata.xgboost:xgboost4j_2.12

JVM Package for XGBoost optimized with Apache Arrow

  • Homepage: https://github.com/Intel-bigdata/xgboost/tree/arrow-to-dmatrix/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.intel.bigdata.xgboost/xgboost4j_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.3.3 (published over 5 years ago)
  • Last Synced: 2026-10-31T16:07:06.950Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 13.915%
    • Dependent packages count: 22.361%
    • Dependent repos count: 31.98%
anaconda.org: r-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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.3 (published over 3 years ago)
  • Last Synced: 2026-03-14T13:16:31.792Z (8 months ago)
  • Versions: 4
  • Dependent Packages: 4
  • Dependent Repositories: 3
  • Downloads: 809 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 6.992%
    • Average: 14.965%
    • Dependent repos count: 46.458%
repo1.maven.org: ml.dmlc:xgboost4j-spark-gpu_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark-gpu_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:40.420Z (2 days ago)
  • Versions: 29
  • Dependent Packages: 0
  • Dependent Repositories: 4
  • Docker Downloads: 162
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Dependent repos count: 12.047%
    • Average: 15.901%
    • Dependent packages count: 49.872%
repo1.maven.org: com.intel.bigdata.xgboost:xgboost4j-flink_2.12

JVM Package for XGBoost optimized with Apache Arrow

  • Homepage: https://github.com/Intel-bigdata/xgboost/tree/arrow-to-dmatrix/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.intel.bigdata.xgboost/xgboost4j-flink_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.3.3 (published over 5 years ago)
  • Last Synced: 2026-10-31T16:06:51.468Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 16.325%
    • Dependent repos count: 31.98%
    • Dependent packages count: 31.998%
repo1.maven.org: com.intel.bigdata.xgboost:xgboost4j-spark_2.12

JVM Package for XGBoost optimized with Apache Arrow

  • Homepage: https://github.com/Intel-bigdata/xgboost/tree/arrow-to-dmatrix/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.intel.bigdata.xgboost/xgboost4j-spark_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.3.3 (published over 5 years ago)
  • Last Synced: 2026-10-31T16:06:49.613Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 16.325%
    • Dependent repos count: 31.98%
    • Dependent packages count: 31.998%
repo1.maven.org: ml.dmlc:xgboost4j-flink_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-flink_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:41.601Z (2 days ago)
  • Versions: 33
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 16.325%
    • Dependent repos count: 31.98%
    • Dependent packages count: 31.998%
repo1.maven.org: ml.dmlc:xgboost4j-flink_2.11

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-flink_2.11/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.1.2 (published almost 6 years ago)
  • Last Synced: 2026-10-31T16:06:45.669Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 16.325%
    • Dependent repos count: 31.98%
    • Dependent packages count: 31.998%
anaconda.org: _r-xgboost-mutex

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 2.0 (published about 4 years ago)
  • Last Synced: 2026-04-02T15:47:45.106Z (7 months ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Downloads: 382 Total
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 13.462%
    • Average: 17.725%
    • Dependent repos count: 51.027%
repo1.maven.org: ml.dmlc:xgboost4j-example

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-example/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 0.90 (published over 7 years ago)
  • Last Synced: 2026-10-31T16:06:27.196Z (2 days ago)
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 18.053%
    • Dependent repos count: 20.657%
    • Dependent packages count: 49.872%
repo1.maven.org: ml.dmlc:xgboost-jvm

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost-jvm/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 2.0.1 (published about 3 years ago)
  • Last Synced: 2026-10-31T16:06:38.783Z (2 days ago)
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 18.053%
    • Dependent repos count: 20.657%
    • Dependent packages count: 49.872%
repo1.maven.org: com.nvidia:xgboost4j-spark_3.0

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.nvidia/xgboost4j-spark_3.0/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.4.2-0.3.0 (published over 4 years ago)
  • Last Synced: 2026-10-31T16:06:47.220Z (2 days ago)
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.773%
    • Stargazers count: 0.911%
    • Average: 18.053%
    • Dependent repos count: 20.657%
    • Dependent packages count: 49.872%
repo1.maven.org: com.intel.bigdata.xgboost:xgboost4j-example_2.12

JVM Package for XGBoost optimized with Apache Arrow

  • Homepage: https://github.com/Intel-bigdata/xgboost/tree/arrow-to-dmatrix/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.intel.bigdata.xgboost/xgboost4j-example_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.3.3 (published over 5 years ago)
  • Last Synced: 2026-10-31T16:06:28.044Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: ml.dmlc:xgboost-jvm_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost-jvm_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:30.502Z (2 days ago)
  • Versions: 32
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: ml.dmlc:xgboost4j-example_2.12

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-example_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:43.580Z (2 days ago)
  • Versions: 33
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: ml.dmlc:xgboost-jvm_2.11

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost-jvm_2.11/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.1.2 (published almost 6 years ago)
  • Last Synced: 2026-10-31T16:06:45.711Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: ml.dmlc:xgboost4j-example_2.11

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-example_2.11/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.1.2 (published almost 6 years ago)
  • Last Synced: 2026-10-31T16:07:03.818Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: com.intel.bigdata.xgboost:xgboost-jvm_2.12

JVM Package for XGBoost optimized with Apache Arrow

  • Homepage: https://github.com/Intel-bigdata/xgboost/tree/arrow-to-dmatrix/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.intel.bigdata.xgboost/xgboost-jvm_2.12/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.3.3 (published over 5 years ago)
  • Last Synced: 2026-10-31T16:07:01.017Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
repo1.maven.org: com.nvidia:xgboost-jvm_3.0

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/com.nvidia/xgboost-jvm_3.0/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 1.4.2-0.3.0 (published over 4 years ago)
  • Last Synced: 2026-10-31T16:06:31.818Z (2 days ago)
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.614%
    • Stargazers count: 0.707%
    • Average: 20.54%
    • Dependent repos count: 31.98%
    • Dependent packages count: 48.86%
nuget.org: libxgboost-2.0.3-win-x64

A package containing the libxgboost native library for win-x64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.0 (published about 2 years ago)
  • Last Synced: 2026-10-31T16:06:32.184Z (2 days ago)
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 12,407 Total
  • Rankings:
    • Dependent repos count: 7.472%
    • Dependent packages count: 20.045%
    • Average: 20.673%
    • Downloads: 34.503%
  • Maintainers (1)
conda-forge.org: r-xgboost-cpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T16:12:03.153Z (7 months ago)
  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 488,249 Total
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda-forge.org: r-xgboost-gpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T16:09:11.574Z (7 months ago)
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 446,996 Total
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda-forge.org: py-xgboost-cpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T16:12:00.537Z (7 months ago)
  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 929,847 Total
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda-forge.org: py-xgboost-gpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.1 (published almost 4 years ago)
  • Last Synced: 2026-08-22T01:27:13.941Z (2 months ago)
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 833,852 Total
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
nuget.org: libxgboost-2.0.3-linux-x64-part1

A package containing part 1 of the libxgboost native library for linux-x64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.0 (published about 2 years ago)
  • Last Synced: 2026-10-31T16:06:31.639Z (2 days ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 17,054 Total
  • Rankings:
    • Dependent repos count: 7.472%
    • Dependent packages count: 20.045%
    • Average: 22.791%
    • Downloads: 40.857%
  • Maintainers (1)
nuget.org: libxgboost-2.0.3-linux-x64-part2

A package containing part 2 of the libxgboost native library for linux-x64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.0 (published about 2 years ago)
  • Last Synced: 2026-10-31T16:06:29.824Z (2 days ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 16,013 Total
  • Rankings:
    • Dependent repos count: 7.472%
    • Dependent packages count: 20.045%
    • Average: 22.899%
    • Downloads: 41.18%
  • Maintainers (1)
nuget.org: libxgboost-2.0.3-linux-x64

A package containing the libxgboost native library for linux-x64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.3 (published over 1 year ago)
  • Last Synced: 2026-10-31T16:06:38.582Z (2 days ago)
  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 13,940 Total
  • Rankings:
    • Dependent repos count: 7.472%
    • Dependent packages count: 20.045%
    • Average: 24.258%
    • Downloads: 45.258%
  • Maintainers (1)
anaconda.org: r-xgboost-cpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 1.7.3 (published over 3 years ago)
  • Last Synced: 2026-03-16T21:20:30.368Z (8 months ago)
  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 730 Total
  • Rankings:
    • Forks count: 2.942%
    • Stargazers count: 3.619%
    • Average: 26.015%
    • Dependent packages count: 39.804%
    • Dependent repos count: 57.694%
anaconda.org: py-xgboost-cpu

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.

  • Homepage: https://github.com/dmlc/xgboost
  • Licenses: Apache-2.0
  • Latest release: 3.3.0 (published 4 months ago)
  • Last Synced: 2026-07-08T19:06:15.089Z (4 months ago)
  • Versions: 15
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 18,150 Total
  • Rankings:
    • Forks count: 2.942%
    • Stargazers count: 3.619%
    • Average: 26.015%
    • Dependent packages count: 39.804%
    • Dependent repos count: 57.694%
nuget.org: libxgboost-2.0.3-osx-arm64

A package containing the libxgboost native library for osx-arm64.

  • Homepage: https://xgboost.readthedocs.io/en/stable/
  • Licenses: apache-2.0
  • Latest release: 1.0.1 (published over 1 year ago)
  • Last Synced: 2026-10-31T16:06:28.779Z (2 days ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 2,562 Total
  • Rankings:
    • Dependent repos count: 7.342%
    • Dependent packages count: 19.698%
    • Average: 28.265%
    • Downloads: 57.755%
  • Maintainers (1)
formulae.brew.sh: xgboost

Scalable, Portable and Distributed Gradient Boosting Library

  • Homepage: https://xgboost.ai/
  • Licenses: Apache-2.0
  • Latest release: 3.4.2 (published 26 days ago)
  • Last Synced: 2026-10-31T16:06:28.474Z (2 days ago)
  • Versions: 32
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 66 Last month
  • Rankings:
    • Dependent packages count: 19.458%
    • Dependent repos count: 29.484%
    • Average: 30.798%
    • Downloads: 43.452%
pypi.org: xgboost-cpu

XGBoost Python Package

  • Homepage:
  • Documentation: https://xgboost-cpu.readthedocs.io/
  • Licenses: Apache-2.0
  • Latest release: 3.4.1 (published 3 months ago)
  • Last Synced: 2026-10-31T16:07:05.157Z (2 days ago)
  • Versions: 19
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 1,518,229 Last month
  • Rankings:
    • Dependent packages count: 10.554%
    • Average: 34.992%
    • Dependent repos count: 59.43%
  • Maintainers (2)
repo1.maven.org: ml.dmlc:xgboost4j-flink_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-flink_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:59.001Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
repo1.maven.org: ml.dmlc:xgboost4j-spark_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:31.092Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
repo1.maven.org: ml.dmlc:xgboost4j_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:55.768Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
repo1.maven.org: ml.dmlc:xgboost4j-example_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-example_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:54.531Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
repo1.maven.org: ml.dmlc:xgboost-jvm_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost-jvm_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:52.976Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
repo1.maven.org: ml.dmlc:xgboost4j-spark-gpu_2.13

JVM Package for XGBoost

  • Homepage: https://github.com/dmlc/xgboost/tree/master/jvm-packages
  • Documentation: https://appdoc.app/artifact/ml.dmlc/xgboost4j-spark-gpu_2.13/
  • Licenses: The Apache License, Version 2.0
  • Latest release: 3.4.0 (published 3 months ago)
  • Last Synced: 2026-10-31T16:06:52.230Z (2 days ago)
  • Versions: 12
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 32.428%
    • Average: 39.393%
    • Dependent packages count: 46.359%
nixpkgs-24.05: python312Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.11: python310Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.05: python311Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

guix: r-xgboost

Extreme gradient boosting

  • Homepage: https://github.com/dmlc/xgboost
  • Documentation: https://git.savannah.gnu.org/cgit/guix.git/tree/gnu/packages/cran.scm#n40291
  • Licenses: asl2.0
  • Latest release: 3.1.3.1 (published 8 months ago)
  • Last Synced: 2026-04-07T12:03:17.862Z (7 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-23.11: python311Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.05: xgboostWithCuda

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

pkgsrc-netbsd-x86_64-10.1-all: math/py-xgboost

XGBoost Python Package

  • Homepage: https://github.com/dmlc/xgboost
  • Documentation: https://pkgsrc.se/math/py-xgboost
  • Licenses: apache-2.0
  • Latest release: 3.2.0 (published 8 months ago)
  • Last Synced: 2026-05-27T08:11:53.001Z (5 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-24.11: xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.05: xgboostWithCuda

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.11: python311Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.05: xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.05: xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.11: xgboostWithCuda

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.11: xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.05: python311Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-23.05: python310Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-24.11: python312Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-unstable: xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

nixpkgs-unstable: python314Packages.xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library

Dependencies

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R-package/DESCRIPTION cran
  • R >= 3.3.0 depends
  • Matrix >= 1.1 imports
  • data.table >= 1.9.6 imports
  • jsonlite >= 1.0 imports
  • methods * 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
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
  • titanic * suggests
  • vcd >= 1.3 suggests
jvm-packages/pom.xml maven
  • org.scala-lang:scala-compiler 2.12.8 provided
  • com.esotericsoftware:kryo 5.4.0
  • commons-logging:commons-logging 1.2
  • org.scala-lang:scala-library 2.12.8
  • org.scala-lang:scala-reflect 2.12.8
  • 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} 2.7.0 compile
  • org.apache.hadoop:hadoop-common ${hadoop.version} provided
  • org.apache.hadoop:hadoop-hdfs ${hadoop.version} provided
  • org.scalatest:scalatest_${scala.binary.version} 3.0.5 provided
  • com.typesafe.akka:akka-testkit_${scala.binary.version} 2.7.0 test
  • junit:junit 4.13.2 test
jvm-packages/xgboost4j-example/pom.xml maven
  • org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
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  • ml.dmlc:xgboost4j-spark_${scala.binary.version} 2.0.0-SNAPSHOT
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jvm-packages/xgboost4j-flink/pom.xml maven
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  • org.apache.flink:flink-ml_${scala.binary.version} ${flink.version}
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jvm-packages/xgboost4j-spark/pom.xml maven
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jvm-packages/xgboost4j-spark-gpu/pom.xml maven
  • ai.rapids:cudf ${cudf.version} provided
  • com.nvidia:rapids-4-spark_${scala.binary.version} ${spark.rapids.version} provided
  • org.apache.spark:spark-core_${scala.binary.version} ${spark.version} provided
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doc/requirements.txt pypi
  • breathe *
  • cloudpickle *
  • graphviz *
  • matplotlib >=2.1
  • mock *
  • numpy *
  • pyspark *
  • recommonmark *
  • scikit-learn *
  • sh >=1.12.14
  • sphinx >=5.2.1
  • sphinx-gallery *
  • sphinx_rtd_theme >=1.0.0
  • xgboost_ray *
python-package/pyproject.toml pypi
  • numpy *
  • scipy *
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