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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.

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Light Gradient Boosting Machine

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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:

  • Faster training speed and higher efficiency.
  • Lower memory usage.
  • Better accuracy.
  • Support of parallel and GPU learning.
  • Capable of handling large-scale data.

For further details, please refer to Features.

Benefitting from these advantages, LightGBM is being widely-used in many winning solutions of machine learning competitions.

Comparison experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, parallel experiments 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 and is generated from this repository. If you are new to LightGBM, follow the installation instructions on that site.

Next you may want to read:

Documentation for contributors:


Please refer to changelogs at GitHub releases page.

Some old update logs are available at Key Events page.

External (Unofficial) Repositories

Optuna (hyperparameter optimization framework):


JPMML (Java PMML converter):

Treelite (model compiler for efficient deployment):

cuML Forest Inference Library (GPU-accelerated inference):

m2cgen (model appliers for various languages):

leaves (Go model applier):

ONNXMLTools (ONNX converter):

SHAP (model output explainer):

MMLSpark (LightGBM on Spark):

Kubeflow Fairing (LightGBM on Kubernetes):

ML.NET (.NET/C#-package):

LightGBM.NET (.NET/C#-package):

Dask-LightGBM (distributed and parallel Python-package):

Ruby gem:

LightGBM4j (Java high-level binding):


How to Contribute


Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] 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". 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". 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". SysML Conference, 2018.

Note: If you use LightGBM in your GitHub projects, please add

in the


This project is licensed under the terms of the MIT license. See LICENSE for additional details.

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