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LightGBM, Light Gradient Boosting Machine

Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data

For more details, please refer to Features.

Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.

News

08/15/2017 : Optimal split for categorical features.

07/13/2017 : Gitter is available.

06/20/2017 : Python-package is on PyPI now.

06/09/2017 : LightGBM Slack team is available.

05/03/2017 : LightGBM v2 stable release.

04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.

02/20/2017 : Update to LightGBM v2.

02/12/2017 : LightGBM v1 stable release.

01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.

12/05/2016 : Categorical Features as input directly (without one-hot coding).

12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.

More detailed update logs : Key Events.

External (unofficial) Repositories

Julia Package: https://github.com/Allardvm/LightGBM.jl

JPMML: https://github.com/jpmml/jpmml-lightgbm

Get Started and Documentation

Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.

Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.

Next you may want to read:

Documentation for contributors:

Support

How to Contribute

LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Reference Paper

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.

About

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

Resources

Readme
MIT license

Code of conduct

Code of conduct
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
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Watchers
1 watching
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0 forks
Report repository
Releases
Packages
Contributors
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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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try {
var __m = "*";
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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
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LightGBM, Light Gradient Boosting Machine
Build StatusWindows Build StatusDocumentation StatusGitHub IssuesLicensePython VersionsPyPI VersionJoin the chat at https://gitter.im/Microsoft/LightGBMSlack
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
  • Faster training speed and higher efficiency
  • Lower memory usage
  • Better accuracy
  • Parallel and GPU learning supported
  • Capable of handling large-scale data
For more details, please refer to Features.
Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the parallel experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
08/15/2017 : Optimal split for categorical features.
07/13/2017 : Gitter is available.
06/20/2017 : Python-package is on PyPI now.
06/09/2017 : LightGBM Slack team is available.
05/03/2017 : LightGBM v2 stable release.
04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our GPU Tutorial and Performance Comparison.
02/20/2017 : Update to LightGBM v2.
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release R-package beta version, welcome to have a try and provide feedback.
12/05/2016 : Categorical Features as input directly (without one-hot coding).
12/02/2016 : Release Python-package beta version, welcome to have a try and provide feedback.
More detailed update logs : Key Events.
External (unofficial) Repositories
Julia Package: https://github.com/Allardvm/LightGBM.jl
JPMML: https://github.com/jpmml/jpmml-lightgbm
Get Started and Documentation
Install by following the guide for the command line program, Python-package or R-package. Then please see the Quick Start guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
Documentation for contributors:
Support
How to Contribute
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
  • Check out call for contributions to see what can be improved, or open an issue if you want something.
  • Contribute to the tests to make it more reliable.
  • Contribute to the documents to make it clearer for everyone.
  • Contribute to the examples to share your experience with other users.
  • Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Reference Paper
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017.
About
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Resources
Readme
MIT license
Code of conduct
Code of conduct
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages