lightgbm-org / LightGBM

> [!NOTE] > This project moved from to in 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 endorsed by the development team. 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 GBNet (use as a PyTorch Module): https://github.com/mthorrell/gbnet 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 supertree (interactive visualization of decision trees): https://github.com/mljar/supertree 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 Ray (distributed computing framework): https://github.com/ray-project/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 LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j Julia-package: https://github.com/IQVIA-ML/LightGBM.jl lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast (R -compliant interface): https://github.com/tidymodels/bonsai (R -compliant interface): https://github.com/mlr-org/mlr3extralearners lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml (run Python-package in a web browser): https://github.com/pyodide/pyodide (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex Support ------- • Ask a question on Stack Overflow with the tag, we monitor this for new questions. • Open **bug reports** and **feature requests** on GitHub issues. How to Contribute -----------------…