XGBoost
Optimized, distributed gradient boosting library widely used for structured/tabular data problems, a staple of applied machine learning.
- Actively maintained
- Well documented
- Permissive license
Overview
XGBoost implements gradient-boosted decision trees with a strong focus on performance and scalability, and has been one of the most consistently winning algorithms in machine learning competitions for structured (tabular) data - the kind of data found in spreadsheets and databases rather than images or text. It runs efficiently on a single machine or distributed across a cluster.
XGBoost is a fit for teams solving classification, regression, or ranking problems on structured data, a different tool than the deep learning frameworks (PyTorch, TensorFlow, also in this catalog) that dominate image, text, and audio tasks but are often outperformed by gradient boosting on tabular data.
Why it's in this catalog
Catalog rating: ★★★★☆ (4/5)
One of the most widely used gradient-boosting libraries, a distinct classical-ML complement to deep-learning frameworks.
- Foundational
- Community-driven
- Categories
- AI & Machine Learning
- Languages
- C++
- License
- Apache-2.0
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