bayesian-optimization/BayesianOptimization

Health v2 · maintenance only

Adoption not part of the score

8696 stars · 1601 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A pure Python library implementing Bayesian global optimization using Gaussian processes. It finds the maximum of expensive, unknown black-box functions in as few iterations as possible by balancing exploration and exploitation.

Use cases

• optimize expensive black-box functions with few evaluations
• tune machine learning model hyperparameters
• find optimal parameters for costly simulations or experiments
• balance exploration and exploitation in parameter search
• maximize an unknown function using gaussian processes

When to choose

• function evaluations are expensive (minutes to hours each)
• the objective function is a black box without gradients
• you need sample-efficient global optimization in Python

When to avoid

• the function is cheap to evaluate (use standard optimizers instead)
• the problem is high-dimensional with many parameters
• you need gradient-based optimization with differentiable objectives

Facets

library · maturity stable

machine-learning math data-science machine-learning data-science python cross-platform bayesian-optimization gaussian-processes hyperparameter-tuning global-optimization algorithms

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markdown · JSON · MCP: product_card(name="bayesian-optimization/BayesianOptimization")