LightGBM
Enrichment pendingA 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 tas
GraphCanon updated today · GitHub synced today
Trust & integrity
Full report- Maintenance
- Very active (1d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Organization account
- As of today · Source: github_public_v1
- Security (OSV)
- No lockfile
- As of today · Source: none
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Overview
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.
Capability facts
- Languages
- c++
Source: github.language · Jul 11, 2026
Categories
Tags
README
<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/LightGBMtolightgbm-org/LightGBMin 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
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 https://lightgbm.readthedocs.io/ 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:
- Examples showing command line usage of common tasks.
- Features and algorithms supported by LightGBM.
- Parameters is an exhaustive list of customization you can make.
- Distributed Learning and GPU Learning can speed up computation.
- FLAML provides automated tuning for LightGBM (code examples).
- Optuna Hyperparameter Tuner provides automated tuning for LightGBM hyperparameters (code examples).
- Understanding LightGBM Parameters (and How to Tune Them using Neptune).
Documentation for contributors:
- How we update readthedocs.io.
- Check out the Development Guide.
News
Please refer to changelogs at GitHub releases page.
External (Unofficial) Repositories
Projects listed here offer alternative ways to use LightGBM. They are not maintained or officially endorse