docs: preserve upstream English README

2026-07-13 10:38:09 +00:00
parent d72d1a58f0
commit c888ad567d
1 changed files with 195 additions and 0 deletions
@@ -0,0 +1,195 @@
+<img src=https://github.com/lightgbm-org/LightGBM/blob/main/docs/logo/LightGBM_logo_black_text.svg width=300 />
+
+> [!NOTE]
+> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026.
+> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM).
+> For details, see https://github.com/lightgbm-org/LightGBM/issues/7187
+
+Light Gradient Boosting Machine
+===============================
+
+[![C++ GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml)
+[![Python-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml)
+[![R-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml)
+[![CUDA Version GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml)
+[![SWIG Wrapper GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml)
+[![Static Analysis GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml)
+[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/main?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/main)
+[![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/)
+[![Link checks](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml)
+[![License](https://img.shields.io/github/license/lightgbm-org/lightgbm.svg)](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE)
+[![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver)
+[![StackOverflow questions](https://img.shields.io/stackexchange/stackoverflow/t/lightgbm?logo=stackoverflow&logoColor=white&label=StackOverflow%20questions)](https://stackoverflow.com/questions/tagged/lightgbm?sort=votes)
+[![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)
+[![conda Version](https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda)](https://anaconda.org/conda-forge/lightgbm)
+[![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm)
+[![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM)
+[![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/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/lightgbm-org/LightGBM/blob/main/docs/Features.rst).
+
+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.
+
+[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.
+
+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/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks.
+- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM.
+- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make.
+- [**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.
+- [**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/)).
+- [**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)).
+- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).
+
+Documentation for contributors:
+
+- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst).
+- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).
+
+News
+----
+
+Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page.
+
+External (Unofficial) Repositories
+----------------------------------
+
+Projects listed here offer alternative ways to use LightGBM.
+They are not maintained or officially endorsed by the `LightGBM` development team.
+
+JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm
+
+Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka
+
+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
+
+GBNet (use `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet
+
+cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml
+
+nvForest (GPU-accelerated inference): https://github.com/rapidsai/nvforest
+
+daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/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
+
+supertree (interactive visualization of decision trees): https://github.com/mljar/supertree
+
+SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML
+
+Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing
+
+Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator
+
+lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray
+
+Ray (distributed computing framework): https://github.com/ray-project/ray
+
+Mars (LightGBM on Mars): https://github.com/mars-project/mars
+
+ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
+
+LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net
+
+LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby
+
+LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j
+
+LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j
+
+Julia-package: https://github.com/IQVIA-ML/LightGBM.jl
+
+lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs
+
+MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer
+
+MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow
+
+FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
+
+MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised
+
+Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna
+
+LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS
+
+LightGBM-MoE (Mixture-of-Experts / regime-switching extension): https://github.com/kyo219/LightGBM-MoE
+
+darts (time series forecasting and anomaly detection with LightGBM): https://github.com/unit8co/darts
+
+mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast
+
+skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast
+
+`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai
+
+`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners
+
+lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform
+
+`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml
+
+`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide
+
+`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex
+
+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** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues).
+
+How to Contribute
+-----------------
+
+Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/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
+----------------
+
+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.
+
+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.
+
+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.
+
+Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018.
+
+License
+-------
+
+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.