Light Gradient Boosting Machine =============================== [](https://github.com/microsoft/LightGBM/actions) [](https://github.com/microsoft/LightGBM/actions) [](https://github.com/microsoft/LightGBM/actions) [](https://github.com/microsoft/LightGBM/actions) [](https://lightgbm-ci.visualstudio.com/lightgbm-ci/_build/latest?definitionId=1) [](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master) [](https://lightgbm.readthedocs.io/) [](https://github.com/microsoft/LightGBM/actions?query=workflow%3A%22Link+checks%22) [](https://github.com/microsoft/LightGBM/blob/master/LICENSE) [](https://pypi.org/project/lightgbm) [](https://pypi.org/project/lightgbm) [](https://cran.r-project.org/package=lightgbm) 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, distributed, 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). Benefiting 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, [distributed learning 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. - [**Distributed 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. - [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/) provides automated tuning for LightGBM ([code examples](https://github.com/microsoft/FLAML/blob/main/notebook/flaml_lightgbm.ipynb)). - [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna/tree/master/examples/lightgbm)). 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 ---------------------------------- FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML 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 lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml daal4py (Intel CPU-accelerated inference): https://github.com/IntelPython/daal4py 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 Shapash (model visualization and interpretation): https://github.com/MAIF/shapash dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz MMLSpark (LightGBM on Spark): https://github.com/Azure/mmlspark Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net Ruby gem: https://github.com/ankane/lightgbm LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j lightgbm-rs (Rust binding): https://github.com/vaaaaanquish/lightgbm-rs MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow `{treesnip}` (R `{parsnip}`-compliant interface): https://github.com/curso-r/treesnip `{mlr3learners.lightgbm}` (R `{mlr3}`-compliant interface): https://github.com/mlr3learners/mlr3learners.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. - 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.