From f3cad95168faaadc71f4968ecf80efe5d99bbe88 Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:38:42 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 148 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 77 insertions(+), 71 deletions(-) diff --git a/README.md b/README.md index 8d3b18f..f050bc6 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,15 @@ + +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/lightgbm-org/LightGBM) · [上游 README](https://github.com/lightgbm-org/LightGBM/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 + > [!NOTE] -> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` 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 +> 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 =============================== @@ -27,50 +33,50 @@ Light Gradient Boosting Machine [![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM) [![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM) -LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: +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： -- Faster training speed and higher efficiency. -- Lower memory usage. -- Better accuracy. -- Support of parallel, distributed, and GPU learning. -- Capable of handling large-scale data. +- 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。 -For further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst). +更多细节请参阅 [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst). -Benefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions. +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)。 -[Comparison experiments](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#comparison-experiment) 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](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings. +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 ----------------------------- -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](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) on that site. +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。 -Next you may want to read: +接下来你可能想阅读： -- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks. -- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM. -- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make. -- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst) can speed up computation. -- [**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/) provides automated tuning for LightGBM ([code examples](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) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)). +- [**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). -Documentation for contributors: +面向贡献者的文档： - [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst). -- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst). +- 查看 [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst). News ---- -Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page. +请参阅 [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases)页面的变更日志。 External (Unofficial) Repositories ---------------------------------- -Projects listed here offer alternative ways to use LightGBM. -They are not maintained or officially endorsed by the `LightGBM` development team. +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 @@ -92,96 +98,96 @@ daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen -leaves (Go model applier): https://github.com/dmitryikh/leaves +leaves（Go 模型应用器）：https://github.com/dmitryikh/leaves -ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools +ONNXMLTools（ONNX 转换器）：https://github.com/onnx/onnxmltools -SHAP (model output explainer): https://github.com/slundberg/shap +SHAP（模型输出解释器）：https://github.com/slundberg/shap -Shapash (model visualization and interpretation): https://github.com/MAIF/shapash +Shapash（模型可视化与解释）：https://github.com/MAIF/shapash -dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz +dtreeviz（决策树可视化与模型解释）：https://github.com/parrt/dtreeviz -supertree (interactive visualization of decision trees): https://github.com/mljar/supertree +supertree（决策树交互式可视化）：https://github.com/mljar/supertree -SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML +SynapseML（Spark 上的 LightGBM）：https://github.com/microsoft/SynapseML -Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing +Kubeflow Fairing（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/fairing -Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator +Kubeflow Operator（Kubernetes 上的 LightGBM）：https://github.com/kubeflow/xgboost-operator -lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray +lightgbm_ray（Ray 上的 LightGBM）：https://github.com/ray-project/lightgbm_ray -Ray (distributed computing framework): https://github.com/ray-project/ray +Ray（分布式计算框架）：https://github.com/ray-project/ray -Mars (LightGBM on Mars): https://github.com/mars-project/mars +Mars（Mars 上的 LightGBM）：https://github.com/mars-project/mars -ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning +ML.NET（.NET/C# 包）：https://github.com/dotnet/machinelearning -LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net +LightGBM.NET（.NET/C# 包）：https://github.com/rca22/LightGBM.Net -LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby +LightGBM Ruby（Ruby gem）：https://github.com/ankane/lightgbm-ruby -LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j +LightGBM4j（Java 高级绑定）：https://github.com/metarank/lightgbm4j -LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/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 +Julia-package：https://github.com/IQVIA-ML/LightGBM.jl -lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs +lightgbm3（Rust 绑定）：https://github.com/Mottl/lightgbm3-rs -MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer +MLServer（LightGBM 推理服务器）：https://github.com/SeldonIO/MLServer -MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow +MLflow（实验跟踪、模型监控框架）：https://github.com/mlflow/mlflow -FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML +FLAML（用于超参数优化的 AutoML 库）：https://github.com/microsoft/FLAML -MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised +MLJAR AutoML（表格数据上的 AutoML）：https://github.com/mljar/mljar-supervised -Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna +Optuna（超参数优化框架）：https://github.com/optuna/optuna -LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS +LightGBMLSS（基于 LightGBM 的概率建模）：https://github.com/StatMixedML/LightGBMLSS -LightGBM-MoE (Mixture-of-Experts / regime-switching extension): https://github.com/kyo219/LightGBM-MoE +LightGBM-MoE（专家混合 / 状态切换扩展）：https://github.com/kyo219/LightGBM-MoE -darts (time series forecasting and anomaly detection with LightGBM): https://github.com/unit8co/darts +darts（使用 LightGBM 进行时间序列预测与异常检测）：https://github.com/unit8co/darts -mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast +mlforecast（使用 LightGBM 进行时间序列预测）：https://github.com/Nixtla/mlforecast -skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast +skforecast（使用 LightGBM 进行时间序列预测）：https://github.com/JoaquinAmatRodrigo/skforecast -`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai +`{bonsai}`（符合 R `{parsnip}` 规范的接口）：https://github.com/tidymodels/bonsai -`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners +`{mlr3extralearners}`（符合 R `{mlr3}` 规范的接口）：https://github.com/mlr-org/mlr3extralearners -lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform +lightgbm-transform（特征转换绑定）：https://github.com/lightgbm-org/LightGBM-transform -`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml +`postgresml`（通过 Postgres 扩展在 SQL 中进行 LightGBM 训练与预测）：https://github.com/postgresml/postgresml -`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide +`pyodide`（在 Web 浏览器中运行 `lightgbm` Python 包）：https://github.com/pyodide/pyodide -`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex +`vaex-ml`（自带 LightGBM 接口的 Python DataFrame 库）：https://github.com/vaexio/vaex -Support +支持 ------- -- Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions. -- Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues). +- 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**。 -How to Contribute +如何贡献 ----------------- -Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) page. +请参阅 [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) 页面。 -Microsoft Open Source Code of Conduct +Microsoft 开源行为准则 ------------------------------------- -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. +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。 -Reference Papers +参考论文 ---------------- -Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees" ([link](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833. +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. @@ -189,7 +195,7 @@ Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018. -License +许可证 ------- -This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE) for additional details. +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)。