xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Science Score: 64.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
27 of 654 committers (4.1%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.9%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
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
Basic Info
- Host: GitHub
- Owner: dmlc
- License: apache-2.0
- Language: C++
- Default Branch: master
- Homepage: https://xgboost.readthedocs.io/
- Size: 33 MB
Statistics
- Stars: 27,285
- Watchers: 900
- Forks: 8,798
- Open Issues: 475
- Releases: 68
Topics
Metadata Files
README.md
eXtreme Gradient Boosting
Community | Documentation | Resources | Contributors | Release Notes
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond billions of examples.
License
© Contributors, 2021. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
Sponsors
Become a sponsor and get a logo here. See details at Sponsoring the XGBoost Project. The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
Open Source Collective sponsors
Sponsors
Backers
Owner
- Name: Distributed (Deep) Machine Learning Community
- Login: dmlc
- Kind: organization
- Repositories: 49
- Profile: https://github.com/dmlc
A Community of Awesome Machine Learning Projects
Citation (CITATION)
@inproceedings{Chen:2016:XST:2939672.2939785,
author = {Chen, Tianqi and Guestrin, Carlos},
title = {{XGBoost}: A Scalable Tree Boosting System},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '16},
year = {2016},
isbn = {978-1-4503-4232-2},
location = {San Francisco, California, USA},
pages = {785--794},
numpages = {10},
url = {http://doi.acm.org/10.1145/2939672.2939785},
doi = {10.1145/2939672.2939785},
acmid = {2939785},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {large-scale machine learning},
}
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Jiaming Yuan | j****n@o****m | 1,710 |
| tqchen | t****n@g****m | 1,404 |
| Philip Hyunsu Cho | c****1@c****u | 520 |
| El Potaeto | p****e@m****m | 252 |
| Tong He | h****7@g****m | 207 |
| Rory Mitchell | r****z@g****m | 169 |
| Nan Zhu | C****t | 168 |
| dependabot[bot] | 4****] | 149 |
| Rong Ou | r****u@g****m | 131 |
| Bobby Wang | w****8@g****m | 122 |
| david-cortes | d****a@g****m | 117 |
| terrytangyuan | t****n@g****m | 106 |
| Vadim Khotilovich | k****h@g****m | 98 |
| James Lamb | j****0@g****m | 75 |
| Dmitry Razdoburdin | d****n@g****m | 51 |
| 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 |
| Boliang Chen | c****u@g****m | 26 |
| Sergei Lebedev | s****y@g****m | 26 |
| Andy Adinets | a****z@g****m | 24 |
| Skipper Seabold | j****d@g****m | 23 |
| pommedeterresautee | S****3 | 23 |
| ShvetsKS | 3****S | 22 |
| Johan Manders | j****n@s****m | 21 |
| Ajinkya Kale | k****a@g****m | 20 |
| sinhrks | s****s@g****m | 20 |
| and 624 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 849
- Total pull requests: 2,694
- Average time to close issues: 5 months
- Average time to close pull requests: 18 days
- Total issue authors: 491
- Total pull request authors: 103
- Average comments per issue: 3.86
- Average comments per pull request: 1.39
- Merged pull requests: 1,877
- Bot issues: 3
- Bot pull requests: 595
Past Year
- Issues: 267
- Pull requests: 975
- Average time to close issues: 11 days
- Average time to close pull requests: 4 days
- Issue authors: 157
- Pull request authors: 42
- Average comments per issue: 2.2
- Average comments per pull request: 1.24
- Merged pull requests: 741
- Bot issues: 3
- Bot pull requests: 90
Top Authors
Issue Authors
- trivialfis (120)
- hcho3 (52)
- david-cortes (46)
- wbo4958 (13)
- yurivict (11)
- NvTimLiu (9)
- jaguerrerod (8)
- mayer79 (6)
- jakirkham (5)
- karl-gardner (4)
- hanfengatonline (4)
- xbanke (4)
- ZiyueXu77 (4)
- jameslamb (4)
- lukezli (3)
Pull Request Authors
- trivialfis (1,112)
- dependabot[bot] (582)
- hcho3 (243)
- david-cortes (196)
- wbo4958 (119)
- razdoburdin (95)
- rongou (61)
- jameslamb (32)
- jakirkham (17)
- mayer79 (15)
- github-actions[bot] (13)
- ZiyueXu77 (11)
- ayoub317 (8)
- UncleLLD (6)
- vepifanov (6)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 58
-
Total downloads:
- pypi 31,936,816 last-month
- nuget 33,716 total
- cran 53,452 last-month
- Total docker downloads: 26,313,783
-
Total dependent packages: 999
(may contain duplicates) -
Total dependent repositories: 14,159
(may contain duplicates) - Total versions: 679
- Total maintainers: 6
pypi.org: xgboost
XGBoost Python Package
- Documentation: https://xgboost.readthedocs.io/
- License: Apache-2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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/
- License: The Apache License, Version 2.0
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Latest release: 0.90
published almost 7 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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/
- License: The Apache License, Version 2.0
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Latest release: 0.90
published almost 7 years ago
Rankings
proxy.golang.org: github.com/dmlc/xgboost
- Documentation: https://pkg.go.dev/github.com/dmlc/xgboost#section-documentation
- License: apache-2.0
-
Latest release: v3.0.4+incompatible
published 6 months ago
Rankings
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
- License: Apache License (== 2.0) | file LICENSE
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Latest release: 0.82.1
published almost 7 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.1.2
published about 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
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Latest release: 1.1.2
published about 5 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 2.0
published over 7 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 3.0.1
published 9 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 0.90
published almost 7 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 3.0.1
published 9 months ago
Rankings
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
- License: Apache-2.0
-
Latest release: 3.0.1
published 9 months ago
Rankings
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
- License: Apache-2.0
-
Latest release: 2.0
published over 7 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 2.1.4
published about 1 year ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.0
published over 7 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.4.2-0.3.0
published almost 4 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.3.3
published almost 5 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.3
published about 3 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.1.2
published about 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.3.3
published almost 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.3.3
published almost 5 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 2.0
published over 3 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 0.90
published almost 7 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 2.0.1
published over 2 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.4.2-0.3.0
published almost 4 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.1.2
published about 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.4.2-0.3.0
published almost 4 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.1.2
published about 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.3.3
published almost 5 years ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 1.3.3
published almost 5 years ago
Rankings
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/
- License: apache-2.0
-
Latest release: 1.0.0
published over 1 year ago
Rankings
Maintainers (1)
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
- License: Apache-2.0
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Latest release: 1.7.1
published over 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.1
published over 3 years ago
Rankings
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/
- License: apache-2.0
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Latest release: 1.0.0
published over 1 year ago
Rankings
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/
- License: apache-2.0
-
Latest release: 1.0.0
published over 1 year ago
Rankings
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/
- License: apache-2.0
-
Latest release: 1.0.3
published 12 months ago
Rankings
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
- License: Apache-2.0
-
Latest release: 1.7.3
published about 3 years ago
Rankings
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
- License: Apache-2.0
-
Latest release: 3.0.1
published 9 months ago
Rankings
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/
- License: apache-2.0
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Latest release: 1.0.1
published 12 months ago
Rankings
Maintainers (1)
pypi.org: xgboost-cpu
XGBoost Python Package
- Documentation: https://xgboost-cpu.readthedocs.io/
- License: Apache-2.0
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Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
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/
- License: The Apache License, Version 2.0
-
Latest release: 3.0.4
published 6 months ago
Rankings
Dependencies
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- ossf/scorecard-action 99c53751e09b9529366343771cc321ec74e9bd3d composite
- 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
- 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
- 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_${scala.binary.version} 2.7.0 test
- junit:junit 4.13.2 test
- org.apache.spark:spark-mllib_${scala.binary.version} ${spark.version} provided
- ml.dmlc:xgboost4j-flink_${scala.binary.version} 2.0.0-SNAPSHOT
- ml.dmlc:xgboost4j-spark_${scala.binary.version} 2.0.0-SNAPSHOT
- org.apache.commons:commons-lang3 3.12.0
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- org.apache.commons:commons-lang3 3.12.0
- org.apache.flink:flink-clients_${scala.binary.version} ${flink.version}
- org.apache.flink:flink-ml_${scala.binary.version} ${flink.version}
- org.apache.flink:flink-scala_${scala.binary.version} ${flink.version}
- org.apache.hadoop:hadoop-common 3.2.4
- com.typesafe.akka:akka-actor_${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_${scala.binary.version} 2.7.0 test
- junit:junit 4.13.2 test
- org.apache.spark:spark-core_${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
- 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
- breathe *
- cloudpickle *
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- matplotlib >=2.1
- mock *
- numpy *
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- recommonmark *
- scikit-learn *
- sh >=1.12.14
- sphinx >=5.2.1
- sphinx-gallery *
- sphinx_rtd_theme >=1.0.0
- xgboost_ray *
- boto3 * test
- cfn_tools * test
- actions/checkout v2 composite
- peter-evans/create-pull-request v5 composite
- numpy *
- scipy *