{"data":{"slug":"lightgbm-org-lightgbm","name":"LightGBM","tagline":"A fast, distributed, high performance gradient boosting framework based on decision tree algorithms.","github_url":"https://github.com/lightgbm-org/LightGBM","owner":"lightgbm-org","repo":"LightGBM","owner_avatar_url":"https://avatars.githubusercontent.com/u/266222920?v=4","primary_language":"C++","stars":18656,"forks":4047,"topics":["data-mining","decision-trees","distributed","gbdt","gbm","gbrt","gradient-boosting","kaggle","lightgbm","machine-learning","parallel","python","r"],"archived":false,"github_pushed_at":"2026-08-01T23:24:20+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/lightgbm-org-lightgbm","markdown_url":"https://www.graphcanon.com/tools/lightgbm-org-lightgbm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/lightgbm-org-lightgbm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=lightgbm-org-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.","homepage_url":"https://lightgbm.readthedocs.io/en/latest/","license":"MIT","open_issues":509,"watchers":418,"ai_summary":"LightGBM is a highly efficient and distributed gradient boosting framework known for its speed, memory efficiency, and accuracy. It supports parallel, distributed, and GPU learning making it suitable for large-scale data processing.","readme_excerpt":"<img src=https://github.com/lightgbm-org/LightGBM/blob/main/docs/logo/LightGBM_logo_black_text.svg width=300 />\n\n> [!NOTE]\n> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` 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://github.com/lightgbm-org/LightGBM/issues/7187\n\nLight Gradient Boosting Machine\n===============================\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst).\n\nBenefiting 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.\n\n[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.\n\nGet Started and Documentation\n-----------------------------\n\nOur 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.\n\nNext you may want to read:\n\n- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks.\n- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM.\n- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make.\n- [**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.\n- [**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/)).\n- [**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)).\n- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).\n\nDocumentation for contributors:\n\n- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst).\n- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).\n\nNews\n----\n\nPlease refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page.\n\nExternal (Unofficial) Repositories\n----------------------------------\n\nProjects listed here offer alternative ways to use LightGBM.\nThey are not maintained or officially endorse","github_created_at":"2016-08-05T05:45:50+00:00","created_at":"2026-07-11T23:23:37.045042+00:00","updated_at":"2026-08-03T06:01:30.540709+00:00","categories":[{"slug":"model-training","name":"Model 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