wehub-resource-sync
196 lines
13 KiB
Markdown
196 lines
13 KiB
Markdown
<img src=https://github.com/lightgbm-org/LightGBM/blob/main/docs/logo/LightGBM_logo_black_text.svg width=300 />
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> [!NOTE]
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> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026.
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> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM).
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> For details, see https://github.com/lightgbm-org/LightGBM/issues/7187
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Light Gradient Boosting Machine
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===============================
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml)
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[](https://ci.appveyor.com/project/guolinke/lightgbm/branch/main)
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[](https://lightgbm.readthedocs.io/)
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[](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml)
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[](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE)
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[](https://jacobtomlinson.dev/effver)
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[](https://stackoverflow.com/questions/tagged/lightgbm?sort=votes)
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[](https://pypi.org/project/lightgbm)
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[](https://pypi.org/project/lightgbm)
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[](https://anaconda.org/conda-forge/lightgbm)
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[](https://cran.r-project.org/package=lightgbm)
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[](https://www.nuget.org/packages/LightGBM)
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[](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM)
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LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
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- Faster training speed and higher efficiency.
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- Lower memory usage.
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- Better accuracy.
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- Support of parallel, distributed, and GPU learning.
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- Capable of handling large-scale data.
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For further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst).
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Benefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
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[Comparison experiments](https://github.com/lightgbm-org/LightGBM/blob/main/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/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
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Get Started and Documentation
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-----------------------------
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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.
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Next you may want to read:
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- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks.
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- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM.
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- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make.
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- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst) can speed up computation.
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- [**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://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)).
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- [**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-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)).
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- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).
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Documentation for contributors:
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- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst).
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- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).
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News
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----
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Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page.
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External (Unofficial) Repositories
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----------------------------------
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Projects listed here offer alternative ways to use LightGBM.
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They are not maintained or officially endorsed by the `LightGBM` development team.
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JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm
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Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka
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Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite
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lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves
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Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird
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GBNet (use `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet
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cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml
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nvForest (GPU-accelerated inference): https://github.com/rapidsai/nvforest
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daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py
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m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen
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leaves (Go model applier): https://github.com/dmitryikh/leaves
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ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools
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SHAP (model output explainer): https://github.com/slundberg/shap
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Shapash (model visualization and interpretation): https://github.com/MAIF/shapash
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dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz
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supertree (interactive visualization of decision trees): https://github.com/mljar/supertree
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SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML
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Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing
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Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator
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lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray
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Ray (distributed computing framework): https://github.com/ray-project/ray
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Mars (LightGBM on Mars): https://github.com/mars-project/mars
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ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
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LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net
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LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby
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LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j
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LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j
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Julia-package: https://github.com/IQVIA-ML/LightGBM.jl
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lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs
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MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer
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MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow
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FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
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MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised
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Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna
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LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS
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LightGBM-MoE (Mixture-of-Experts / regime-switching extension): https://github.com/kyo219/LightGBM-MoE
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darts (time series forecasting and anomaly detection with LightGBM): https://github.com/unit8co/darts
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mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast
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skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast
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`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai
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`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners
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lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform
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`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml
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`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide
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`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex
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Support
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-------
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- Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
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- Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues).
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How to Contribute
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-----------------
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Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) page.
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Microsoft Open Source Code of Conduct
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-------------------------------------
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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.
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Reference Papers
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----------------
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Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees" ([link](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.
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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://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.
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Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html)". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.
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Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018.
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License
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-------
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This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE) for additional details.
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