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LightGBM
The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.

About LightGBM
LightGBM is an open-source gradient boosting framework based on decision-tree algorithms for ranking, classification and other machine learning tasks. It supports parallel, distributed and GPU learning and is designed to handle large-scale data.
The project moved from Microsoft/LightGBM to lightgbm-org/LightGBM in March 2026 and remains the official source maintained by the same maintainers. The repository includes examples, parameter lists and documentation references, while listed third-party converters and deployment tools are unofficial.
Key features
- Tree-based gradient boosting for ranking and classification tasks
- Parallel, distributed and GPU learning support
- Designed to handle large-scale data
- Command-line examples for common tasks
- Exhaustive parameter list for customization
- Code examples for automated tuning with FLAML and Optuna
- Supports parallel, distributed and GPU learning
- Handles large-scale data
- Open-source code with usage examples and parameter reference
LightGBM at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Windows
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Popular in AI Data & Analytics
Well-known listings from the same topic.
LightGBM FAQ
Is this still the official LightGBM repository after the move?
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