Bayesian Optimization
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
What is Bayesian Optimization?
BayesianOptimization is a Python library for performing constrained global optimization using Bayesian inference and Gaussian processes. It is designed to efficiently find the maximum of an unknown, expensive-to-evaluate function by balancing exploration of new areas with exploitation of known promising regions.
Data scientists, machine learning engineers, and researchers who need to optimize high-cost black-box functions, such as hyperparameter tuning for machine learning models or simulation-based optimization.
Developers choose BayesianOptimization because it minimizes the number of function evaluations required, reducing computational costs, and provides a robust, pure-Python implementation with support for advanced features like constrained optimization and non-float parameters.
Overview
A Python implementation of global optimization with gaussian processes.
Use Cases
Best For
- Hyperparameter tuning for machine learning models where each evaluation is computationally expensive
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