# lightgbm を Homebrew, Nix, MacPorts, winget でインストール lightgbm のインストール経路、実行ファイル、メタデータ、AI エージェント向けセキュリティノートを確認します。 ## インストール ```sh sudo av install brew:lightgbm ``` 追加のインストールコマンド: ### macOS - Homebrew (100%): ```sh brew install lightgbm ``` 証拠: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install LightGBM ``` 証拠: MacPorts ports tree: math/LightGBM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ### Linux - Nix (92%): ```sh nix profile install nixpkgs#lightgbm ``` 証拠: nixpkgs package indexes: pkgs/by-name/li/lightgbm/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 ### Windows - winget (92%): ```sh winget install --id Microsoft.LightGBM -e ``` 証拠: Windows Package Manager source index: Microsoft.LightGBM from https://cdn.winget.microsoft.com/cache/source.msix ## パッケージ情報 - **パッケージキー:** brew:lightgbm - **パッケージマネージャ:** Homebrew - **パッケージマネージャページ:** - **バージョン:** 4.7.0 - **ソース概要:** Fast, distributed, high performance gradient boosting framework - **ホームページ:** - **リポジトリ:** - **上流ドキュメント:** - **ライセンス:** MIT - **ソースアーカイブ:** - **最終更新:** 2026-07-18T21:02:43Z - **生成日時:** 2026-08-04T22:13:35+00:00 ## 実行可能ファイル - lightgbm (cli) - lightgbm (エイリアス) ## 依存関係 - libomp ## ビルド依存関係 - cmake ## インストール挙動 - post-install フック: 未定義 - Bottle: 利用可能 対象 arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux ## バージョンと鮮度 - ページ生成日: 2026-08-04 - マネージャ版: 4.7.0 - マネージャ更新日: 2026-07-18 - ローカルデータ: OK - 上流リポジトリ: https://github.com/lightgbm-org/LightGBM - 情報: No cached GitHub release or tag data was available. ## プロジェクトの歴史と使われ方 LightGBM, short for Light Gradient Boosting Machine, is a high-performance gradient boosting framework for tree-based learning. It became one of the standard packages for tabular machine learning because it combines fast histogram-based training, low memory use, categorical-feature handling, and parallel or distributed execution. ### プロジェクトの歴史 The GitHub repository was created on 2016-08-05. The README describes LightGBM as a gradient boosting framework designed for faster training, lower memory usage, better accuracy, parallel and distributed learning, GPU learning, and large-scale data. The official papers connect the implementation to Microsoft Research work on communication-efficient parallel decision trees in 2016 and the 2017 NIPS paper "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." The paper introduced Gradient-based One-Side Sampling and Exclusive Feature Bundling as key techniques for speeding up GBDT training. The repository moved from Microsoft/LightGBM to lightgbm-org/LightGBM in March 2026. The maintainers documented the move in issue 7187 and stated that the same maintainers, including the creator of LightGBM, continued managing the official source repository. ### 採用の歴史 The README says LightGBM has been widely used in winning machine-learning competition solutions. Its package footprint spans command-line binaries, Python, R, conda, CRAN, NuGet, Winget, Homebrew, and downstream integrations such as Spark-oriented wrappers and inference converters. LightGBM's adoption followed a practical need in tabular-data workflows: teams wanted XGBoost-class accuracy with faster training iterations and better memory behavior on large datasets. ### 使われ方 Users train models from the CLI or language bindings for regression, classification, ranking, and large-scale distributed tasks. Common package-manager use cases include installing the CLI for experiments, installing Python or R bindings for notebooks and pipelines, and installing the library as a dependency of higher-level ML systems. ### パッケージ好きにとっての重要性 LightGBM is package-nerd significant because it is a research system that became packaging infrastructure: native C++, Python wheels, R packages, GPU builds, distributed modes, and many downstream wrappers all have to agree on the same fast tree learner. It is also a canonical example of ML packaging complexity, where one upstream project must serve CLI users, language-binding users, GPU users, and distro maintainers without losing performance-sensitive native code paths. ### タイムライン - 2016-08-05: GitHub repository created. - 2016: NIPS paper on communication-efficient parallel decision trees published by LightGBM authors. - 2017-02-27: Official experiment documentation records the first version of comparison and parallel experiments. - 2017: NIPS paper "LightGBM: A Highly Efficient Gradient Boosting Decision Tree" published. - 2018: GPU acceleration paper cited by the README. - 2020-03-08: Official experiment documentation updated against a then-new master branch. - 2022: Quantized training paper cited by the README. - 2026-03: Repository moved from Microsoft/LightGBM to lightgbm-org/LightGBM. ### Related projects - Related projects include XGBoost, scikit-learn integrations, SynapseML, FLAML, Optuna, Treelite, SHAP, ML.NET, ONNX conversion tools, and many language bindings listed by the LightGBM README. ### ソース - - - - - - - - ## セキュリティノート narrow executable package without higher-risk signals. - **Geiger リスク:** グリーン / 低 - narrow executable package without higher-risk signals ## ソースデータベース詳細 - **Source Database:** Homebrew formula API - **Tap:** homebrew/core - **Full Name:** lightgbm - **Version Scheme:** 0 - **Revision:** 0 - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** stable ## 他のパッケージマネージャ記録 - Nix - lightgbm: normalized package name match | nixpkgs package indexes: pkgs/by-name/li/lightgbm/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 - MacPorts - LightGBM: normalized package name match | MacPorts ports tree: math/LightGBM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 - winget - Microsoft.LightGBM: normalized package name match | Windows Package Manager source index: Microsoft.LightGBM from https://cdn.winget.microsoft.com/cache/source.msix ## 関連リンク - [MCP tool packages](https://pkg.so/ja/mcp-tools/) - Mentions MCP or Model Context Protocol. - [AI and agent packages](https://pkg.so/ja/ai-agent-tools/) - Matched AI model, agent, coding-agent, orchestration, or MCP metadata. - [Terminal utility packages](https://pkg.so/ja/terminal-utilities/) - Matched terminal and command-line workflow metadata. - [Networking and protocol packages](https://pkg.so/ja/networking-protocol-tools/) - Matched network, protocol, or remote-service metadata. - [cmake](https://pkg.so/ja/brew/cmake/) - Build dependency declared by Homebrew. - [tesseract](https://pkg.so/ja/brew/tesseract/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [opencv](https://pkg.so/ja/brew/opencv/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [pytorch](https://pkg.so/ja/brew/pytorch/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [mlx](https://pkg.so/ja/brew/mlx/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [hf](https://pkg.so/ja/brew/hf/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [libtensorflow](https://pkg.so/ja/brew/libtensorflow/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [sentencepiece](https://pkg.so/ja/brew/sentencepiece/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. - [ncnn](https://pkg.so/ja/brew/ncnn/) - Shares pkgdb curated category or tags: ai, cli, machine-learning, ml-tools. ## Combined YAML source View the package source record on GitHub. [combined/lightgbm.yml](https://github.com/mxcl/pkgdb/blob/main/combined/lightgbm.yml) ## ソース - pkg.so package database - Geiger risk classifier - package-page enrichment - curated package history - package version freshness - pkgdb category and tag curation - package relationship graph - external package-manager database matches - cross-ecosystem install command graph