# bayesian-optimization/BayesianOptimization

A Python implementation of global optimization with gaussian processes.

Repository: https://github.com/bayesian-optimization/BayesianOptimization
Canonical: https://ross.abutalabs.com/products/bayesianoptimization
Homepage: https://bayesian-optimization.github.io/BayesianOptimization/index.html
Language: Python
License: MIT
License Family: permissive
Topics: optimization, gaussian-processes, bayesian-optimization, python, simple
Last push: 2026-08-21T14:54:17+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 74, longevity 100
- inputs: {"age_days": 4471, "days_push": 12, "days_rel": 95, "gap_med": 32, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8696, forks 1601 (observed 2026-08-28T04:10:24.272458+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: machine-learning, math, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: bayesian-optimization, gaussian-processes, hyperparameter-tuning, global-optimization, algorithms

## Member repositories
- bayesian-optimization/BayesianOptimization (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:24.272458+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:25:34.242500+00:00, confidence not recorded.
  - readme: https://github.com/bayesian-optimization/BayesianOptimization (fetched 2026-08-28T04:10:24.272458+00:00, sha a8627286ace8)
  - homepage: https://bayesian-optimization.github.io/BayesianOptimization/index.html (fetched 2026-08-29T08:25:52.423222+00:00, sha 5909659e3fcf)
- Data as of 2026-08-30T08:39:29.467469+00:00.