https://github.com/amir22010/lightgbm
A fast, distributed, high performance gradient boosting (GBT, 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.
Science Score: 10.0%
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Low similarity (14.7%) to scientific vocabulary
Last synced: 9 months ago
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A fast, distributed, high performance gradient boosting (GBT, 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.
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Fork of microsoft/LightGBM
Created almost 7 years ago
· Last pushed almost 7 years ago
https://github.com/Amir22010/LightGBM/blob/master/
LightGBM, Light Gradient Boosting Machine ========================================= [](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_build/latest?definitionId=1) [](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master) [](https://travis-ci.org/microsoft/LightGBM) [](https://lightgbm.readthedocs.io/) [](https://github.com/microsoft/LightGBM/blob/master/LICENSE) [](https://pypi.org/project/lightgbm) [](https://pypi.org/project/lightgbm) [](https://gitter.im/Microsoft/LightGBM?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [](https://lightgbm-slack-autojoin.herokuapp.com) 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. - Support of parallel and GPU learning. - Capable of handling large-scale data. For further details, please refer to [Features](https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst). Benefitting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions. [Comparison experiments](https://github.com/microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [parallel experiments](https://github.com/microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings. Get Started and Documentation ----------------------------- Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. If you are new to LightGBM, follow [the installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) on that site. Next you may want to read: * [**Examples**](https://github.com/microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks. * [**Features**](https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM. * [**Parameters**](https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make. * [**Parallel Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation. * [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters. Documentation for contributors: * [**How we update readthedocs.io**](https://github.com/microsoft/LightGBM/blob/master/docs/README.rst). * Check out the [**Development Guide**](https://github.com/microsoft/LightGBM/blob/master/docs/Development-Guide.rst). News ---- 08/15/2017 : Optimal split for categorical features. 07/13/2017 : [Gitter](https://gitter.im/Microsoft/LightGBM) is available. 06/20/2017 : Python-package is on [PyPI](https://pypi.org/project/lightgbm) now. 06/09/2017 : [LightGBM Slack team](https://lightgbm.slack.com) is available. 05/03/2017 : LightGBM v2 stable release. 04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our [GPU Tutorial](./docs/GPU-Tutorial.rst) and [Performance Comparison](./docs/GPU-Performance.rst). 02/20/2017 : Update to LightGBM v2. 02/12/2017 : LightGBM v1 stable release. 01/08/2017 : Release [**R-package**](https://github.com/microsoft/LightGBM/tree/master/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**](https://github.com/microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback. More detailed update logs : [Key Events](https://github.com/microsoft/LightGBM/blob/master/docs/Key-Events.md). External (Unofficial) Repositories ---------------------------------- Julia-package: https://github.com/Allardvm/LightGBM.jl JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools SHAP (model output explainer): https://github.com/slundberg/shap MMLSpark (Spark-package): https://github.com/Azure/mmlspark ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net Dask-LightGBM (distributed and parallel Python-package): https://github.com/dask/dask-lightgbm Support ------- * Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions. * Discuss on the [LightGBM Gitter](https://gitter.im/Microsoft/LightGBM). * Discuss on the [LightGBM Slack team](https://lightgbm.slack.com). * Use [this invite link](https://lightgbm-slack-autojoin.herokuapp.com/) to join the team. * Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/microsoft/LightGBM/issues). 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. - Contribute to the [tests](https://github.com/microsoft/LightGBM/tree/master/tests) to make it more reliable. - Contribute to the [documentation](https://github.com/microsoft/LightGBM/tree/master/docs) to make it clearer for everyone. - Contribute to the [examples](https://github.com/microsoft/LightGBM/tree/master/examples) to share your experience with other users. - Look for [issues with tag "help wanted"](https://github.com/microsoft/LightGBM/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) and submit pull requests to address them. - Add your stories and experience to [Awesome LightGBM](https://github.com/microsoft/LightGBM/blob/master/examples/README.md). If LightGBM helped you in a machine learning competition or some research application, we want to hear about it! - [Open an issue](https://github.com/microsoft/LightGBM/issues) to report problems or recommend new features. Microsoft Open Source Code of Conduct ------------------------------------- This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. Reference Papers ---------------- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157. Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](http://papers.nips.cc/paper/6380-a-communication-efficient-parallel-algorithm-for-decision-tree)". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287. Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018. **Note**: If you use LightGBM in your GitHub projects, please add `lightgbm` in the `requirements.txt`. License ------- This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/microsoft/LightGBM/blob/master/LICENSE) for additional details.
Owner
- Name: Amir Khan
- Login: Amir22010
- Kind: user
- Location: India
- Repositories: 3
- Profile: https://github.com/Amir22010
working on developing a state of art AI solutions mainly in computer vision, chat bots and nlp domain. building an awesome AI as a professional developer 😍.