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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, distributed, and GPU learning.
  • Capable of handling large-scale data.

For further details, please refer to Features.

Benefiting 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, distributed learning 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

Projects listed here offer alternative ways to use LightGBM. They are not maintained or officially endorsed by the LightGBM development team.

LightGBMLSS (An extension of LightGBM to probabilistic modelling from which prediction intervals and quantiles can be derived):

FLAML (AutoML library for hyperparameter optimization):

Optuna (hyperparameter optimization framework):


JPMML (Java PMML converter):

Nyoka (Python PMML converter):

Treelite (model compiler for efficient deployment):

lleaves (LLVM-based model compiler for efficient inference):

Hummingbird (model compiler into tensor computations):

cuML Forest Inference Library (GPU-accelerated inference):

daal4py (Intel CPU-accelerated inference):

m2cgen (model appliers for various languages):

leaves (Go model applier):

ONNXMLTools (ONNX converter):

SHAP (model output explainer):

Shapash (model visualization and interpretation):

dtreeviz (decision tree visualization and model interpretation):

SynapseML (LightGBM on Spark):

Kubeflow Fairing (LightGBM on Kubernetes):

Kubeflow Operator (LightGBM on Kubernetes):

lightgbm_ray (LightGBM on Ray):

Mars (LightGBM on Mars):

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

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

Ruby gem:

LightGBM4j (Java high-level binding):

lightgbm-rs (Rust binding):

MLflow (experiment tracking, model monitoring framework):

{bonsai} (R {parsnip}-compliant interface):

{mlr3extralearners} (R {mlr3}-compliant interface):

lightgbm-transform (feature transformation binding):

postgresml (LightGBM training and prediction in SQL, via a Postgres extension):

vaex-ml (Python DataFrame library with its own interface to LightGBM):


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 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). 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". 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.


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