# LightGBM **Repository Path**: calllal/LightGBM ## Basic Information - **Project Name**: LightGBM - **Description**: 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. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-12-21 - **Last Updated**: 2021-12-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README LightGBM, Light Gradient Boosting Machine ========================================= [![GitHub Actions Build Status](https://github.com/microsoft/LightGBM/workflows/GitHub%20Actions/badge.svg?branch=master)](https://github.com/microsoft/LightGBM/actions) [![Azure Pipelines Build Status](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_apis/build/status/Microsoft.LightGBM?branchName=master)](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_build/latest?definitionId=1) [![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/master?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master) [![Travis Build Status](https://travis-ci.org/microsoft/LightGBM.svg?branch=master)](https://travis-ci.org/microsoft/LightGBM) [![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/) [![License](https://img.shields.io/github/license/microsoft/lightgbm.svg)](https://github.com/microsoft/LightGBM/blob/master/LICENSE) [![Python Versions](https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white)](https://pypi.org/project/lightgbm) [![PyPI Version](https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white)](https://pypi.org/project/lightgbm) [![Join Gitter at https://gitter.im/Microsoft/LightGBM](https://badges.gitter.im/Microsoft/LightGBM.svg)](https://gitter.im/Microsoft/LightGBM?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Slack](https://lightgbm-slack-autojoin.herokuapp.com/badge.svg)](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. - [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM 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 ---- Please refer to changelogs at [GitHub releases](https://github.com/microsoft/LightGBM/releases) page. Some old update logs are available at [Key Events](https://github.com/microsoft/LightGBM/blob/master/docs/Key-Events.md) page. External (Unofficial) Repositories ---------------------------------- Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna Julia-package: https://github.com/IQVIA-ML/LightGBM.jl JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen leaves (Go model applier): https://github.com/dmitryikh/leaves ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools SHAP (model output explainer): https://github.com/slundberg/shap MMLSpark (LightGBM on Spark): https://github.com/Azure/mmlspark Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing 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 Ruby gem: https://github.com/ankane/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 ----------------- Check [CONTRIBUTING](https://github.com/microsoft/LightGBM/blob/master/CONTRIBUTING.md) page. 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.