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.
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.
Please refer to changelogs at GitHub releases page.
Some old update logs are available at Key Events page.
Projects listed here offer alternative ways to use LightGBM.
They are not maintained or officially endorsed by the LightGBM development team.
FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna
Julia-package: https://github.com/IQVIA-ML/LightGBM.jl
JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm
Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka
Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite
lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves
Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird
cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml
daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py
m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen
leaves (Go model applier): https://github.com/dmitryikh/leaves
ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools
SHAP (model output explainer): https://github.com/slundberg/shap
Shapash (model visualization and interpretation): https://github.com/MAIF/shapash
dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz
SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML
Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing
Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator
lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray
Mars (LightGBM on Mars): https://github.com/mars-project/mars
ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net
Ruby gem: https://github.com/ankane/lightgbm-ruby
LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j
lightgbm-rs (Rust binding): https://github.com/vaaaaanquish/lightgbm-rs
MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow
{bonsai} (R {parsnip}-compliant interface): https://github.com/tidymodels/bonsai
{mlr3extralearners} (R {mlr3}-compliant interface): https://github.com/mlr-org/mlr3extralearners
lightgbm-transform (feature transformation binding): https://github.com/microsoft/lightgbm-transform
postgresml (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml
vaex-ml (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex
- Ask a question on Stack Overflow with the
lightgbmtag, we monitor this for new questions. - Open bug reports and feature requests (not questions) on GitHub issues.
Check CONTRIBUTING page.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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.
Note: If you use LightGBM in your GitHub projects, please add lightgbm in the requirements.txt.
This project is licensed under the terms of the MIT license. See LICENSE for additional details.