NGBoost
A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.
What is NGBoost?
NGBoost is a Python library that implements Natural Gradient Boosting for probabilistic prediction, enabling machine learning models to output full probability distributions rather than single-point estimates. It solves the problem of uncertainty quantification in predictions by providing a scalable and modular framework built on scikit-learn.
Data scientists and machine learning practitioners who need probabilistic forecasts and uncertainty estimates in regression tasks, particularly those working with scikit-learn-based workflows.
Developers choose NGBoost for its efficient natural gradient boosting approach, which improves probabilistic prediction stability, and its seamless integration with scikit-learn, offering flexibility in scoring rules, distributions, and learners.
Overview
Natural Gradient Boosting for Probabilistic Prediction
Use Cases
Best For
- Probabilistic regression tasks requiring uncertainty intervals
- Integrating probabilistic prediction into scikit-learn pipelines
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
