/README.md

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> [!NOTE]
> 本项目已于 2026 年 3 月从 `Microsoft/LightGBM` 迁移至 `lightgbm-org/LightGBM`。
> 本仓库仍是 LightGBM 的官方源代码，由同一批维护者（包括 LightGBM 的创建者）管理。
> 详情请参阅 https://github.com/lightgbm-org/LightGBM/issues/7187

Light Gradient Boosting Machine
===============================

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LightGBM 是一个采用基于树的学习算法的梯度提升（gradient boosting）框架。它面向分布式与高效场景设计，具有以下优势：

- 更快的训练速度与更高的效率。
- 更低的内存占用。
- 更好的准确率。
- 支持并行、分布式与 GPU 学习。
- 能够处理大规模数据。

更多细节请参阅 [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst).

得益于这些优势，LightGBM 已广泛应用于众多机器学习竞赛的[获奖方案](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions)。

在公开数据集上的[对比实验](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#comparison-experiment)表明，LightGBM 在效率与准确率方面均可优于现有 boosting 框架，且内存消耗显著更低。此外，[分布式学习实验](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment)表明，在特定设置下，LightGBM 可通过使用多台机器进行训练实现线性加速。

Get Started and Documentation
-----------------------------

我们的主要文档位于 https://lightgbm.readthedocs.io/，由本仓库生成。如果你是 LightGBM 新手，请按照该站点上的[安装说明](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)操作。

接下来你可能想阅读：

- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples)展示常见任务的命令行用法。
- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst)以及 LightGBM 支持的算法。
- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst)是可进行自定义的详尽参数列表。
- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst)与 [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst)可加速计算。
- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/)为 LightGBM 提供自动化调参（[代码示例](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)).
- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258)为 LightGBM 超参数提供自动化调参（[代码示例](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)).
- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).

面向贡献者的文档：

- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst).
- 查看 [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst).

News
----

请参阅 [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases)页面的变更日志。

External (Unofficial) Repositories
----------------------------------

此处列出的项目提供了使用 LightGBM 的替代方式。
它们并非由 `LightGBM` 开发团队维护或官方背书。

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 `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet

cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml

nvForest (GPU-accelerated inference): https://github.com/rapidsai/nvforest

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 模型应用器）：https://github.com/dmitryikh/leaves

ONNXMLTools（ONNX 转换器）：https://github.com/onnx/onnxmltools

SHAP（模型输出解释器）：https://github.com/slundberg/shap

Shapash（模型可视化与解释）：https://github.com/MAIF/shapash

dtreeviz（决策树可视化与模型解释）：https://github.com/parrt/dtreeviz

supertree（决策树交互式可视化）：https://github.com/mljar/supertree

SynapseML（Spark 上的 LightGBM）：https://github.com/microsoft/SynapseML

Kubeflow Fairing（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/fairing

Kubeflow Operator（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/xgboost-operator

lightgbm_ray（Ray 上的 LightGBM）：https://github.com/ray-project/lightgbm_ray

Ray（分布式计算框架）：https://github.com/ray-project/ray

Mars（Mars 上的 LightGBM）：https://github.com/mars-project/mars

ML.NET（.NET/C# 包）：https://github.com/dotnet/machinelearning

LightGBM.NET（.NET/C# 包）：https://github.com/rca22/LightGBM.Net

LightGBM Ruby（Ruby gem）：https://github.com/ankane/lightgbm-ruby

LightGBM4j（Java 高级绑定）：https://github.com/metarank/lightgbm4j

LightGBM4J（用 Scala 编写的 LightGBM JVM 接口）：https://github.com/seek-oss/lightgbm4j

Julia-package：https://github.com/IQVIA-ML/LightGBM.jl

lightgbm3（Rust 绑定）：https://github.com/Mottl/lightgbm3-rs

MLServer（LightGBM 推理服务器）：https://github.com/SeldonIO/MLServer

MLflow（实验跟踪、模型监控框架）：https://github.com/mlflow/mlflow

FLAML（用于超参数优化的 AutoML 库）：https://github.com/microsoft/FLAML

MLJAR AutoML（表格数据上的 AutoML）：https://github.com/mljar/mljar-supervised

Optuna（超参数优化框架）：https://github.com/optuna/optuna

LightGBMLSS（基于 LightGBM 的概率建模）：https://github.com/StatMixedML/LightGBMLSS

LightGBM-MoE（专家混合 / 状态切换扩展）：https://github.com/kyo219/LightGBM-MoE

darts（使用 LightGBM 进行时间序列预测与异常检测）：https://github.com/unit8co/darts

mlforecast（使用 LightGBM 进行时间序列预测）：https://github.com/Nixtla/mlforecast

skforecast（使用 LightGBM 进行时间序列预测）：https://github.com/JoaquinAmatRodrigo/skforecast

`{bonsai}`（符合 R `{parsnip}` 规范的接口）：https://github.com/tidymodels/bonsai

`{mlr3extralearners}`（符合 R `{mlr3}` 规范的接口）：https://github.com/mlr-org/mlr3extralearners

lightgbm-transform（特征转换绑定）：https://github.com/lightgbm-org/LightGBM-transform

`postgresml`（通过 Postgres 扩展在 SQL 中进行 LightGBM 训练与预测）：https://github.com/postgresml/postgresml

`pyodide`（在 Web 浏览器中运行 `lightgbm` Python 包）：https://github.com/pyodide/pyodide

`vaex-ml`（自带 LightGBM 接口的 Python DataFrame 库）：https://github.com/vaexio/vaex

支持
-------

- 在 [Stack Overflow 上使用 `lightgbm` 标签提问](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
- 在 [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues). 上提交 **bug 报告** 和 **功能请求**。

如何贡献
-----------------

请参阅 [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) 页面。

Microsoft 开源行为准则
-------------------------------------

本项目已采纳 [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 更多信息请参阅 [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)，或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们提出其他问题或意见。

参考论文
----------------

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees"（[链接](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). 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](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)". 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](https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html)". 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](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018.

许可证
-------

本项目依据 MIT 许可证条款授权。更多详情请参阅 [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE)。