[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9594":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":40,"readmeContent":41,"aiSummary":42,"trendingCount":16,"starSnapshotCount":16,"syncStatus":43,"lastSyncTime":44,"discoverSource":45},9594,"LightGBM","lightgbm-org\u002FLightGBM","lightgbm-org","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.","https:\u002F\u002Flightgbm.readthedocs.io\u002Fen\u002Flatest\u002F",null,"C++",18450,4028,418,443,0,1,25,111,11,45,"MIT License",false,"master",[26,27,28,29,30,31,32,33,34,35,36,37,38,39],"data-mining","decision-trees","distributed","gbdt","gbm","gbrt","gradient-boosting","kaggle","lightgbm","machine-learning","microsoft","parallel","python","r","2026-06-12 02:02:09","\u003Cimg src=https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002Flogo\u002FLightGBM_logo_black_text.svg width=300 \u002F>\n\n> [!NOTE]\n> This project moved from `Microsoft\u002FLightGBM` to `lightgbm-org\u002FLightGBM` in March 2026.\n> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM).\n> For details, see https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fissues\u002F7187\n\nLight Gradient Boosting Machine\n===============================\n\n[![C++ GitHub Actions Build Status](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fcpp.yml\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fcpp.yml)\n[![Python-package GitHub Actions Build Status](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fpython_package.yml\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fpython_package.yml)\n[![R-package GitHub Actions Build Status](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fr_package.yml\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Fr_package.yml)\n[![CUDA Version GitHub Actions Build 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Status](https:\u002F\u002Fci.appveyor.com\u002Fapi\u002Fprojects\u002Fstatus\u002F1ys5ot401m0fep6l\u002Fbranch\u002Fmaster?svg=true)](https:\u002F\u002Fci.appveyor.com\u002Fproject\u002Fguolinke\u002Flightgbm\u002Fbranch\u002Fmaster)\n[![Documentation Status](https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Flightgbm\u002Fbadge\u002F?version=latest)](https:\u002F\u002Flightgbm.readthedocs.io\u002F)\n[![Link checks](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Flychee.yml\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Factions\u002Fworkflows\u002Flychee.yml)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Flightgbm-org\u002Flightgbm.svg)](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002FLICENSE)\n[![EffVer Versioning](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fversion_scheme-EffVer-0097a7)](https:\u002F\u002Fjacobtomlinson.dev\u002Feffver)\n[![StackOverflow questions](https:\u002F\u002Fimg.shields.io\u002Fstackexchange\u002Fstackoverflow\u002Ft\u002Flightgbm?logo=stackoverflow&logoColor=white&label=StackOverflow%20questions)](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Flightgbm?sort=votes)\n[![Python Versions](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Flightgbm.svg?logo=python&logoColor=white)](https:\u002F\u002Fpypi.org\u002Fproject\u002Flightgbm)\n[![PyPI Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Flightgbm.svg?logo=pypi&logoColor=white)](https:\u002F\u002Fpypi.org\u002Fproject\u002Flightgbm)\n[![conda Version](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Flightgbm?logo=conda-forge&logoColor=white&label=conda)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Flightgbm)\n[![CRAN Version](https:\u002F\u002Fwww.r-pkg.org\u002Fbadges\u002Fversion\u002Flightgbm)](https:\u002F\u002Fcran.r-project.org\u002Fpackage=lightgbm)\n[![NuGet Version](https:\u002F\u002Fimg.shields.io\u002Fnuget\u002Fv\u002Flightgbm?logo=nuget&logoColor=white)](https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002FLightGBM)\n[![Winget Version](https:\u002F\u002Fimg.shields.io\u002Fwinget\u002Fv\u002FMicrosoft.LightGBM)](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fwinget-pkgs\u002Ftree\u002Fmaster\u002Fmanifests\u002Fm\u002FMicrosoft\u002FLightGBM)\n\nLightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:\n\n- Faster training speed and higher efficiency.\n- Lower memory usage.\n- Better accuracy.\n- Support of parallel, distributed, and GPU learning.\n- Capable of handling large-scale data.\n\nFor further details, please refer to [Features](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FFeatures.rst).\n\nBenefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fexamples\u002FREADME.md#machine-learning-challenge-winning-solutions) of machine learning competitions.\n\n[Comparison experiments](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FExperiments.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:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FExperiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.\n\nGet Started and Documentation\n-----------------------------\n\nOur primary documentation is at https:\u002F\u002Flightgbm.readthedocs.io\u002F and is generated from this repository. If you are new to LightGBM, follow [the installation instructions](https:\u002F\u002Flightgbm.readthedocs.io\u002Fen\u002Flatest\u002FInstallation-Guide.html) on that site.\n\nNext you may want to read:\n\n- [**Examples**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Ftree\u002Fmaster\u002Fexamples) showing command line usage of common tasks.\n- [**Features**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FFeatures.rst) and algorithms supported by LightGBM.\n- [**Parameters**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FParameters.rst) is an exhaustive list of customization you can make.\n- [**Distributed Learning**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FParallel-Learning-Guide.rst) and [**GPU Learning**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FGPU-Tutorial.rst) can speed up computation.\n- [**FLAML**](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Ffast-and-lightweight-automl-for-large-scale-data\u002Farticles\u002Fflaml-a-fast-and-lightweight-automl-library\u002F) provides automated tuning for LightGBM ([code examples](https:\u002F\u002Fmicrosoft.github.io\u002FFLAML\u002Fdocs\u002FExamples\u002FAutoML-for-LightGBM\u002F)).\n- [**Optuna Hyperparameter Tuner**](https:\u002F\u002Fmedium.com\u002Foptuna\u002Flightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https:\u002F\u002Fgithub.com\u002Foptuna\u002Foptuna-examples\u002Fblob\u002Fmain\u002Flightgbm\u002Flightgbm_tuner_simple.py)).\n- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https:\u002F\u002Fneptune.ai\u002Fblog\u002Flightgbm-parameters-guide).\n\nDocumentation for contributors:\n\n- [**How we update readthedocs.io**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FREADME.rst).\n- Check out the [**Development Guide**](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002Fdocs\u002FDevelopment-Guide.rst).\n\nNews\n----\n\nPlease refer to changelogs at [GitHub releases](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Freleases) page.\n\nExternal (Unofficial) Repositories\n----------------------------------\n\nProjects listed here offer alternative ways to use LightGBM.\nThey are not maintained or officially endorsed by the `LightGBM` development team.\n\nJPMML (Java PMML converter): https:\u002F\u002Fgithub.com\u002Fjpmml\u002Fjpmml-lightgbm\n\nNyoka (Python PMML converter): https:\u002F\u002Fgithub.com\u002FSoftwareAG\u002Fnyoka\n\nTreelite (model compiler for efficient deployment): https:\u002F\u002Fgithub.com\u002Fdmlc\u002Ftreelite\n\nlleaves (LLVM-based model compiler for efficient inference): https:\u002F\u002Fgithub.com\u002Fsiboehm\u002Flleaves\n\nHummingbird (model compiler into tensor computations): https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhummingbird\n\nGBNet (use `LightGBM` as a [PyTorch Module](https:\u002F\u002Fdocs.pytorch.org\u002Fdocs\u002Fstable\u002Fgenerated\u002Ftorch.nn.Module.html)): https:\u002F\u002Fgithub.com\u002Fmthorrell\u002Fgbnet\n\ncuML Forest Inference Library (GPU-accelerated inference): https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcuml\n\ndaal4py (Intel CPU-accelerated inference): https:\u002F\u002Fgithub.com\u002Fintel\u002Fscikit-learn-intelex\u002Ftree\u002Fmaster\u002Fdaal4py\n\nm2cgen (model appliers for various languages): https:\u002F\u002Fgithub.com\u002FBayesWitnesses\u002Fm2cgen\n\nleaves (Go model applier): https:\u002F\u002Fgithub.com\u002Fdmitryikh\u002Fleaves\n\nONNXMLTools (ONNX converter): https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnxmltools\n\nSHAP (model output explainer): https:\u002F\u002Fgithub.com\u002Fslundberg\u002Fshap\n\nShapash (model visualization and interpretation): https:\u002F\u002Fgithub.com\u002FMAIF\u002Fshapash\n\ndtreeviz (decision tree visualization and model interpretation): https:\u002F\u002Fgithub.com\u002Fparrt\u002Fdtreeviz\n\nsupertree (interactive visualization of decision trees): https:\u002F\u002Fgithub.com\u002Fmljar\u002Fsupertree\n\nSynapseML (LightGBM on Spark): https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FSynapseML\n\nKubeflow Fairing (LightGBM on Kubernetes): https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Ffairing\n\nKubeflow Operator (LightGBM on Kubernetes): https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fxgboost-operator\n\nlightgbm_ray (LightGBM on Ray): https:\u002F\u002Fgithub.com\u002Fray-project\u002Flightgbm_ray\n\nRay (distributed computing framework): https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray\n\nMars (LightGBM on Mars): https:\u002F\u002Fgithub.com\u002Fmars-project\u002Fmars\n\nML.NET (.NET\u002FC#-package): https:\u002F\u002Fgithub.com\u002Fdotnet\u002Fmachinelearning\n\nLightGBM.NET (.NET\u002FC#-package): https:\u002F\u002Fgithub.com\u002Frca22\u002FLightGBM.Net\n\nLightGBM Ruby (Ruby gem): https:\u002F\u002Fgithub.com\u002Fankane\u002Flightgbm-ruby\n\nLightGBM4j (Java high-level binding): https:\u002F\u002Fgithub.com\u002Fmetarank\u002Flightgbm4j\n\nLightGBM4J (JVM interface for LightGBM written in Scala): https:\u002F\u002Fgithub.com\u002Fseek-oss\u002Flightgbm4j\n\nJulia-package: https:\u002F\u002Fgithub.com\u002FIQVIA-ML\u002FLightGBM.jl\n\nlightgbm3 (Rust binding): https:\u002F\u002Fgithub.com\u002FMottl\u002Flightgbm3-rs\n\nMLServer (inference server for LightGBM): https:\u002F\u002Fgithub.com\u002FSeldonIO\u002FMLServer\n\nMLflow (experiment tracking, model monitoring framework): https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow\n\nFLAML (AutoML library for hyperparameter optimization): https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FFLAML\n\nMLJAR AutoML (AutoML on tabular data): https:\u002F\u002Fgithub.com\u002Fmljar\u002Fmljar-supervised\n\nOptuna (hyperparameter optimization framework): https:\u002F\u002Fgithub.com\u002Foptuna\u002Foptuna\n\nLightGBMLSS (probabilistic modelling with LightGBM): https:\u002F\u002Fgithub.com\u002FStatMixedML\u002FLightGBMLSS\n\nLightGBM-MoE (Mixture-of-Experts \u002F regime-switching extension): https:\u002F\u002Fgithub.com\u002Fkyo219\u002FLightGBM-MoE\n\nmlforecast (time series forecasting with LightGBM): https:\u002F\u002Fgithub.com\u002FNixtla\u002Fmlforecast\n\nskforecast (time series forecasting with LightGBM): https:\u002F\u002Fgithub.com\u002FJoaquinAmatRodrigo\u002Fskforecast\n\n`{bonsai}` (R `{parsnip}`-compliant interface): https:\u002F\u002Fgithub.com\u002Ftidymodels\u002Fbonsai\n\n`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https:\u002F\u002Fgithub.com\u002Fmlr-org\u002Fmlr3extralearners\n\nlightgbm-transform (feature transformation binding): https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM-transform\n\n`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https:\u002F\u002Fgithub.com\u002Fpostgresml\u002Fpostgresml\n\n`pyodide` (run `lightgbm` Python-package in a web browser): https:\u002F\u002Fgithub.com\u002Fpyodide\u002Fpyodide\n\n`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https:\u002F\u002Fgithub.com\u002Fvaexio\u002Fvaex\n\nSupport\n-------\n\n- Ask a question [on Stack Overflow with the `lightgbm` tag](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Fask?tags=lightgbm), we monitor this for new questions.\n- Open **bug reports** and **feature requests** on [GitHub issues](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fissues).\n\nHow to Contribute\n-----------------\n\nCheck [CONTRIBUTING](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md) page.\n\nMicrosoft Open Source Code of Conduct\n-------------------------------------\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002F). For more information see the [Code of Conduct FAQ](https:\u002F\u002Fopensource.microsoft.com\u002Fcodeofconduct\u002Ffaq\u002F) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n\nReference Papers\n----------------\n\nYu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. \"Quantized Training of Gradient Boosting Decision Trees\" ([link](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2022\u002Fhash\u002F77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.\n\nGuolin 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:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2017\u002Fhash\u002F6449f44a102fde848669bdd9eb6b76fa-Abstract.html)\". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.\n\nQi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. \"[A Communication-Efficient Parallel Algorithm for Decision Tree](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2016\u002Fhash\u002F10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html)\". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.\n\nHuan Zhang, Si Si and Cho-Jui Hsieh. \"[GPU Acceleration for Large-scale Tree Boosting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.08359)\". SysML Conference, 2018.\n\nLicense\n-------\n\nThis project is licensed under the terms of the MIT license. See [LICENSE](https:\u002F\u002Fgithub.com\u002Flightgbm-org\u002FLightGBM\u002Fblob\u002Fmaster\u002FLICENSE) for additional details.\n","LightGBM是一个基于决策树算法的快速、分布式、高性能梯度提升框架，适用于排名、分类等多种机器学习任务。其核心功能包括高效的梯度提升决策树（GBDT）训练、支持大规模数据处理以及并行计算，显著提升了模型训练速度和预测性能。技术特点上，LightGBM采用了直方图优化、叶子节点生长策略等创新方法来减少内存使用和提高效率。此外，它还支持多种编程语言接口如Python和R，便于不同背景的数据科学家和工程师使用。该工具非常适合需要在短时间内处理大量数据并对模型性能有高要求的应用场景，例如在线广告点击率预测、金融风险评估等领域。",2,"2026-06-11 03:23:38","top_topic"]