BayesO
The facts below are what the maker publishes about BayesO, checked and dated. Our editors have not written about it yet.
Plans and prices
From BayesO's own pricing page, checked 12 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| BayesO | Free | MIT-licensed Python package |
The facts
Each one read from BayesO's own pages, with the date it was checked.
What it does
- Acquisition functions
- Included acquisition functions include probability of improvement, expected improvement, and Gaussian process upper confidence bound.checked 12 Oct 2026
- Bayesian analysis
- Yeschecked 23 Sep 2026
- Hyperparameter optimization
- An example uses BayesO to optimize max_depth and n_estimators for an XGBoost classifier.checked 12 Oct 2026
- Installation
- The package can be installed from PyPI with pip install bayeso or from source, including in editable development mode.checked 12 Oct 2026
- Integrations
- The documentation includes a script and example for optimizing a function with WandB.checked 12 Oct 2026
- Intended users
- The documentation describes use for application-specific modifications and for running Bayesian optimization in various applications.checked 12 Oct 2026
- License
- BayesO is licensed under the MIT License.checked 12 Oct 2026
- Optimization use
- Bayesian optimization is presented as a strategy for global optimization of black-box and expensive-to-evaluate functions.checked 12 Oct 2026
- Optional packages
- Optional, development, and example dependencies can be installed with the optional, dev, and examples extras.checked 12 Oct 2026
- Other acquisition options
- The API also lists log expected improvement, augmented expected improvement, pure exploitation, and pure exploration.checked 12 Oct 2026
- Purpose
- BayesO is a Python package for Bayesian optimization, designed for advanced or straightforward use across applications.checked 12 Oct 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Security and compliance
- The opened documentation pages do not state security or compliance certifications.checked 12 Oct 2026
- Supported Python
- The project says it tests Python versions 3.8 through 3.12.checked 12 Oct 2026
- Surrogate models
- The package includes Gaussian process regression and Student-t process regression surrogate functions.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
