scikit-optimize
Sequential model-based optimization toolbox.
Decision gist · record as of 2026-08-14
Yes, with caution on maintenance. Scikit-Optimize is a solid, permissively licensed tool for black-box optimization with low install friction and no known vulnerabilities. However, the last release was 801 days ago and maintenance is dormant. If your use case is stable and you do not need recent dependency updates or bug fixes, it is safe to use; if you need active support or compatibility with the latest Python or dependency versions, verify that the fork's current state meets your needs before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel distribution.
- Maintenance is dormant—the last release was 801 days ago, though the repository remains active and the maintainer has PyPI credentials.
- Dependencies are stable and well-established (joblib, numpy, scipy, scikit-learn).
License · maintenance · safety
BSD 3-clause (permissive) — BSD 3-clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
last release 2024-06-04 (801 days) · last repo commit 2024-06-04 · 24 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 810,537 downloads/mo, #5,008 on PyPI
Alternatives
Verify before relying
pip install scikit-optimize
import numpy as np
from scikit-optimize import gp_minimize
def f(x):
return (numpy.sin(5 * x[0]) * (1 - numpy.tanh(x[0] ** 2)) +
numpy.random.randn() * 0.1)
res = gp_minimize(f, [(-2.0, 2.0)])- Whether the dormant maintenance status (801 days since last release) affects compatibility with recent Python or dependency versions
- Current state of open issues and pull requests on the fork repository
- Whether plotting functionality requires explicit installation of the [plots] extra
What it is and what it does
Scikit-Optimize implements sequential model-based optimization for functions that are expensive to evaluate, noisy, or lack gradient information. It builds on numpy, scipy, and scikit-learn to provide methods like Gaussian process minimization that iteratively suggest promising parameter values, evaluate them, and refine the model. The library is designed for hyperparameter tuning, experimental design, and other settings where you need to find good inputs without access to derivatives or with high evaluation cost.
The package offers both high-level functions for simple use cases and lower-level Optimizer classes for fine-grained control over the optimization loop. It includes plotting utilities for visualizing the objective function landscape and optimization progress, available via an optional extra that adds matplotlib.
Use it for
- Tune machine learning model hyperparameters when grid or random search is too slow
- Optimize expensive simulation or experimental parameters with noisy outcomes
- Find optimal configurations for systems where each evaluation takes significant time or resources
- Explore high-dimensional parameter spaces without gradient information
- Visualize and understand objective function behavior across parameter ranges
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with caution on maintenance.
Scikit-Optimize is a solid, permissively licensed tool for black-box optimization with low install friction and no known vulnerabilities. However, the last release was 801 days ago and maintenance is dormant. If your use case is stable and you do not need recent dependency updates or bug fixes, it is safe to use; if you need active support or compatibility with the latest Python or dependency versions, verify that the fork's current state meets your needs before committing.
Install
scikit-optimize on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Maintenance is dormant—the last release was 801 days ago, though the repository remains active and the maintainer has PyPI credentials. Dependencies are stable and well-established (joblib, numpy, scipy, scikit-learn).
License in practice
BSD 3-clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
Quickstart
pip install scikit-optimize
import numpy as np
from scikit-optimize import gp_minimize
def f(x):
return (numpy.sin(5 * x[0]) * (1 - numpy.tanh(x[0] ** 2)) +
numpy.random.randn() * 0.1)
res = gp_minimize(f, [(-2.0, 2.0)])Verify before relying
- Whether the dormant maintenance status (801 days since last release) affects compatibility with recent Python or dependency versions
- Current state of open issues and pull requests on the fork repository
- Whether plotting functionality requires explicit installation of the [plots] extra
Package facts
| License | BSD 3-clause permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesjoblibpyamlnumpyscipyscikit-learnpackaging |
| Maintenance | Dormant 801 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 810,537 / month, #5,008 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: scikit_optimize-0.10.2-py2.py3-none-any.whl
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