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https://github.com/dmlc/xgboost

distributed-systems gbdt gbm gbrt machine-learning xgboost

Last synced: about 3 hours ago

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:

Committers metadata

Last synced: 3 days ago

Total Commits: 6,505
Total Committers: 675
Avg Commits per committer: 9.637
Development Distribution Score (DDS): 0.766

Commits in past year: 550
Committers in past year: 34
Avg Commits per committer in past year: 16.176
Development Distribution Score (DDS) in past year: 0.536

Name Email Commits
Jiaming Yuan j****n@o****m 1525
tqchen t****n@g****m 1115
Philip Hyunsu Cho c****1@c****u 494
El Potaeto p****e@m****m 249
Tong He h****7@g****m 207
Tianqi Chen t****n 198
Rory Mitchell r****z@g****m 169
dependabot[bot] 4****] 144
Rong Ou r****u@g****m 131
Bobby Wang w****8@g****m 113
Nan Zhu C****t 113
Vadim Khotilovich k****h@g****m 98
tqchen w****w@g****m 91
James Lamb j****0@g****m 73
david-cortes d****a@g****m 70
terrytangyuan t****n@g****m 67
CodingCat z****u@g****m 55
nachocano n****o@g****m 48
Yuan (Terry) Tang t****n 36
kalenhaha c****2@g****m 34
trivialfis y****s@h****m 33
[email protected] t****n@g****m 32
giuliohome g****e@g****m 31
Faron f****z@g****m 30
AbdealiJK a****i@g****m 28
Boliang Chen c****u@g****m 26
Sergei Lebedev s****y@g****m 26
Skipper Seabold j****d@g****m 23
pommedeterresautee S****3 23
Dmitry Razdoburdin d****n@g****m 22
and 645 more...

Issue and Pull Request metadata

Last synced: 1 day ago

Package metadata

pypi: xgboost

XGBoost Python Package

  • Homepage:
  • Documentation: https://xgboost.readthedocs.io/
  • Licenses: Apache-2.0
  • Latest release: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:45:56.647Z (1 day ago)
  • Versions: 80
  • Dependent Packages: 684
  • Dependent Repositories: 12,601
  • Downloads: 27,055,410 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 (4)
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T13:34:14.093Z (12 days ago)
  • Versions: 24
  • Dependent Packages: 23
  • Dependent Repositories: 221
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 1.887%
    • Dependent repos count: 2.251%
    • Dependent packages count: 2.9%
maven: 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 5 years ago)
  • Last Synced: 2024-11-08T22:47:01.808Z (1 day 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%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:45:47.725Z (1 day ago)
  • Versions: 23
  • 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: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:37:59.002Z (11 days ago)
  • Versions: 18
  • Dependent Packages: 13
  • Dependent Repositories: 122
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 2.573%
    • Dependent repos count: 3.08%
    • Dependent packages count: 4.817%
maven: 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 5 years ago)
  • Last Synced: 2024-11-08T22:46:59.556Z (1 day 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%
go: github.com/dmlc/xgboost

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/dmlc/xgboost#section-documentation
  • Licenses: apache-2.0
  • Latest release: v2.0.3+incompatible (published 11 months ago)
  • Last Synced: 2024-11-08T22:45:42.335Z (1 day ago)
  • Versions: 34
  • 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: 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 5 years ago)
  • Last Synced: 2024-11-08T22:46:18.996Z (1 day ago)
  • Versions: 35
  • Dependent Packages: 145
  • Dependent Repositories: 520
  • Downloads: 57,234 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)
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:45:32.911Z (1 day ago)
  • Versions: 23
  • 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%
maven: 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 4 years ago)
  • Last Synced: 2024-11-08T22:47:12.146Z (1 day 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%
maven: 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 4 years ago)
  • Last Synced: 2024-11-08T22:46:55.365Z (1 day 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: 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 about 2 years ago)
  • Last Synced: 2024-11-08T22:47:26.942Z (1 day ago)
  • Versions: 18
  • Dependent Packages: 4
  • Dependent Repositories: 37
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 5.2%
    • Dependent repos count: 5.915%
    • Dependent packages count: 12.488%
conda: _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 6 years ago)
  • Last Synced: 2024-10-29T19:37:51.884Z (11 days ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 36
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Dependent repos count: 6.039%
    • Average: 7.004%
    • Dependent packages count: 19.581%
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:38:03.896Z (11 days ago)
  • Versions: 18
  • Dependent Packages: 7
  • Dependent Repositories: 3
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 7.08%
    • Dependent packages count: 8.011%
    • Dependent repos count: 17.914%
conda: 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: 2.0.3 (published 7 months ago)
  • Last Synced: 2024-10-29T19:37:45.066Z (11 days ago)
  • Versions: 12
  • Dependent Packages: 2
  • Dependent Repositories: 122
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 9.299%
    • Dependent packages count: 13.462%
    • Dependent repos count: 17.326%
maven: 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 5 years ago)
  • Last Synced: 2024-11-08T22:46:29.191Z (1 day 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%
conda: 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: 2.0.3 (published 7 months ago)
  • Last Synced: 2024-10-29T19:37:48.413Z (11 days ago)
  • Versions: 12
  • Dependent Packages: 4
  • Dependent Repositories: 37
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 6.992%
    • Average: 10.028%
    • Dependent repos count: 26.71%
conda: 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: 2.0.3 (published 7 months ago)
  • Last Synced: 2024-11-08T22:48:00.961Z (1 day ago)
  • Versions: 7
  • Dependent Packages: 3
  • Dependent Repositories: 221
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 10.242%
    • Dependent repos count: 13.033%
    • Dependent packages count: 21.527%
conda: _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 6 years ago)
  • Last Synced: 2024-10-29T19:37:54.903Z (11 days ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.988%
    • Stargazers count: 1.408%
    • Average: 11.52%
    • Dependent packages count: 19.581%
    • Dependent repos count: 24.103%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:46:10.686Z (1 day ago)
  • Versions: 19
  • 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%
conda: _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 6 years ago)
  • Last Synced: 2024-11-08T22:47:39.737Z (1 day ago)
  • Versions: 2
  • Dependent Packages: 2
  • Dependent Repositories: 36
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Average: 11.73%
    • Dependent packages count: 13.462%
    • Dependent repos count: 27.047%
maven: 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 2 years ago)
  • Last Synced: 2024-11-08T22:45:36.365Z (1 day 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%
maven: 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 3 years ago)
  • Last Synced: 2024-11-08T22:46:27.022Z (1 day 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%
conda: 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 almost 2 years ago)
  • Last Synced: 2024-11-08T22:47:31.840Z (1 day ago)
  • Versions: 4
  • Dependent Packages: 4
  • Dependent Repositories: 3
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 6.992%
    • Average: 14.965%
    • Dependent repos count: 46.458%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:47:25.895Z (1 day ago)
  • Versions: 19
  • 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%
maven: 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 3 years ago)
  • Last Synced: 2024-11-08T22:47:24.741Z (1 day 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%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:46:20.059Z (1 day ago)
  • Versions: 23
  • 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%
maven: 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 3 years ago)
  • Last Synced: 2024-11-08T22:46:27.376Z (1 day 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%
maven: 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 4 years ago)
  • Last Synced: 2024-11-08T22:46:07.910Z (1 day 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%
conda: _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 over 2 years ago)
  • Last Synced: 2024-10-29T19:37:51.251Z (11 days ago)
  • Versions: 1
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 2.791%
    • Stargazers count: 3.619%
    • Dependent packages count: 13.462%
    • Average: 17.725%
    • Dependent repos count: 51.027%
maven: 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 2 years ago)
  • Last Synced: 2024-11-08T22:47:24.594Z (1 day 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%
maven: 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 1 year ago)
  • Last Synced: 2024-11-08T22:46:21.919Z (1 day 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%
maven: 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 5 years ago)
  • Last Synced: 2024-11-08T22:46:58.556Z (1 day 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%
maven: 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 4 years ago)
  • Last Synced: 2024-11-08T22:46:36.232Z (1 day 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%
maven: 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 2 years ago)
  • Last Synced: 2024-11-08T22:47:19.807Z (1 day 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%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:46:21.822Z (1 day ago)
  • Versions: 23
  • 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%
maven: 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 3 years ago)
  • Last Synced: 2024-11-08T22:46:48.313Z (1 day 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%
maven: 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 4 years ago)
  • Last Synced: 2024-11-08T22:46:55.744Z (1 day 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%
maven: 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 3 years ago)
  • Last Synced: 2024-11-08T22:47:00.115Z (1 day 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%
maven: 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: 2.0.3 (published 11 months ago)
  • Last Synced: 2024-11-08T22:47:07.006Z (1 day ago)
  • Versions: 22
  • 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%
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:38:02.054Z (11 days ago)
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:38:00.656Z (11 days ago)
  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:37:50.935Z (11 days ago)
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda: 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 about 2 years ago)
  • Last Synced: 2024-10-29T19:37:53.521Z (11 days ago)
  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 0.953%
    • Stargazers count: 1.323%
    • Average: 21.869%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
conda: 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 almost 2 years ago)
  • Last Synced: 2024-11-08T22:47:36.183Z (1 day ago)
  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 2.942%
    • Stargazers count: 3.619%
    • Average: 26.015%
    • Dependent packages count: 39.804%
    • Dependent repos count: 57.694%
conda: 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: 2.0.3 (published 7 months ago)
  • Last Synced: 2024-11-08T22:47:57.593Z (1 day ago)
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Forks count: 2.942%
    • Stargazers count: 3.619%
    • Average: 26.015%
    • Dependent packages count: 39.804%
    • Dependent repos count: 57.694%
pypi: xgboost-cpu

XGBoost Python Package

  • Homepage:
  • Documentation: https://xgboost-cpu.readthedocs.io/
  • Licenses: Apache-2.0
  • Latest release:
  • Last Synced: 2024-11-08T22:45:43.829Z (1 day ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 12,408 Last month
  • Rankings:
    • Dependent packages count: 10.554%
    • Average: 34.992%
    • Dependent repos count: 59.43%
  • Maintainers (2)

Dependencies

.github/workflows/jvm_tests.yml actions
  • actions/cache 937d24475381cd9c75ae6db12cb4e79714b926ed composite
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.github/workflows/main.yml actions
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.github/workflows/python_tests.yml actions
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.github/workflows/r_tests.yml actions
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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} ${spark.version} provided
  • ml.dmlc:xgboost4j-flink_${scala.binary.version} 2.0.0-SNAPSHOT
  • ml.dmlc:xgboost4j-spark_${scala.binary.version} 2.0.0-SNAPSHOT
  • org.apache.commons:commons-lang3 3.12.0
jvm-packages/xgboost4j-flink/pom.xml maven
  • ml.dmlc:xgboost4j_${scala.binary.version} 2.0.0-SNAPSHOT
  • org.apache.commons:commons-lang3 3.12.0
  • 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} ${flink.version}
  • org.apache.hadoop:hadoop-common 3.2.4
jvm-packages/xgboost4j-gpu/pom.xml maven
  • com.typesafe.akka:akka-actor_${scala.binary.version} 2.7.0 compile
  • ai.rapids:cudf ${cudf.version} provided
  • 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
  • org.apache.commons:commons-lang3 3.12.0
  • com.typesafe.akka:akka-testkit_${scala.binary.version} 2.7.0 test
  • junit:junit 4.13.2 test
jvm-packages/xgboost4j-spark/pom.xml maven
  • org.apache.spark:spark-core_${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} ${spark.version} provided
  • ml.dmlc:xgboost4j_${scala.binary.version} 2.0.0-SNAPSHOT
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} ${spark.version} provided
  • org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
  • org.apache.spark:spark-sql_${scala.binary.version} ${spark.version} provided
  • ml.dmlc:xgboost4j-gpu_${scala.binary.version} 2.0.0-SNAPSHOT
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 *
tests/buildkite/infrastructure/requirements.txt pypi
  • boto3 * test
  • cfn_tools * test
.github/workflows/update_rapids.yml actions
  • actions/checkout v2 composite
  • peter-evans/create-pull-request v5 composite
python-package/pyproject.toml pypi
  • numpy *
  • scipy *