Comparison
RoBO vs scikit-optimize
Verdict
Pick RoBO if roBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests; pick scikit-optimize if scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.
Markdown twin · RoBO alternatives · scikit-optimize alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | RoBO | scikit-optimize |
|---|---|---|
| Maintenance | Dormant (2653d since push) As of 2w · github_public_v1 | Archived (893d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- RoBO
- A Robust Bayesian Optimization framework
- scikit-optimize
- Sequential model-based optimization library with scipy.optimize interface
Stars
- RoBO
- 492
- scikit-optimize
- 2.8k
Forks
- RoBO
- 129
- scikit-optimize
- 559
Open issues
- RoBO
- 25
- scikit-optimize
- 318
Language
- RoBO
- Python
- scikit-optimize
- Python
Adopt for
- RoBO
- RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests.
- scikit-optimize
- Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.
Persona
- RoBO
- -
- scikit-optimize
- -
Runtime
- RoBO
- -
- scikit-optimize
- -
License
- RoBO
- BSD-3-Clause
- scikit-optimize
- BSD-3-Clause
Last pushed
- RoBO
- Apr 30, 2019
- scikit-optimize
- Feb 23, 2024
Categories
- RoBO
- Model Training
- scikit-optimize
- Model Training
Trust and health
Maintenance
- RoBO
- Dormant (18%)
- scikit-optimize
- Archived (8%)
Days since push
- RoBO
- 2653d
- scikit-optimize
- 893d
Archived on GitHub
- RoBO
- No
- scikit-optimize
- Yes
Open issues (now)
- RoBO
- 25
- scikit-optimize
- 318
Full report
- RoBO
- Trust report
- scikit-optimize
- Trust report
Shared compatibility
- Python · RoBO: Python runtime · scikit-optimize: Python runtime
Choose RoBO if…
- Tags unique to RoBO: gaussian processes, python, random forests.
- For tasks requiring robust handling of noisy data in Bayesian Optimization
- Leaner open-issue backlog (25).
When NOT to use RoBO
- Avoid if your project strictly requires open-source licenses other than BSD-3-Clause
- Not suitable for users not comfortable installing external dependencies manually
Choose scikit-optimize if…
- Tags unique to scikit-optimize: hyperparameter-tuning, machine-learning, scikit-learn.
- Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.
- More GitHub stars (2.8k vs 492) - visibility, not fit.
When NOT to use scikit-optimize
- Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient.
- Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments.
- Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/RoBO) · observed Aug 4, 2026
- GitHub forks (automl/RoBO) · observed Aug 4, 2026
- Last push (automl/RoBO) · observed Apr 30, 2019
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (scikit-optimize/scikit-optimize) · observed Aug 4, 2026
- GitHub forks (scikit-optimize/scikit-optimize) · observed Aug 4, 2026
- Last push (scikit-optimize/scikit-optimize) · observed Feb 23, 2024
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: RoBO 492 · scikit-optimize 2.8k (synced Aug 4, 2026).
Common questions
- What is the difference between RoBO and scikit-optimize?
- RoBO: A Robust Bayesian Optimization framework. scikit-optimize: Sequential model-based optimization library with scipy.optimize interface. See the comparison table for live GitHub stats and shared categories.
- When should I choose RoBO over scikit-optimize?
- Choose RoBO over scikit-optimize when Tags unique to RoBO: gaussian processes, python, random forests; For tasks requiring robust handling of noisy data in Bayesian Optimization; Leaner open-issue backlog (25).
- When should I choose scikit-optimize over RoBO?
- Choose scikit-optimize over RoBO when Tags unique to scikit-optimize: hyperparameter-tuning, machine-learning, scikit-learn; Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective; More GitHub stars (2.8k vs 492) - visibility, not fit.
- When should I avoid RoBO?
- Avoid if your project strictly requires open-source licenses other than BSD-3-Clause Not suitable for users not comfortable installing external dependencies manually
- When should I avoid scikit-optimize?
- Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient. Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments. Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.
- Is RoBO or scikit-optimize more popular on GitHub?
- scikit-optimize has more GitHub stars (2,829 vs 492). Stars measure visibility, not whether either tool fits your constraints.
- Are RoBO and scikit-optimize open source?
- Yes - both are open-source projects on GitHub (RoBO: BSD-3-Clause, scikit-optimize: BSD-3-Clause).
- Where can I find alternatives to RoBO or scikit-optimize?
- GraphCanon lists graph-backed alternatives at RoBO alternatives and scikit-optimize alternatives (RoBO markdown twin, scikit-optimize markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, RoBO or scikit-optimize?
- RoBO: Dormant. scikit-optimize: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for RoBO and scikit-optimize?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: RoBO trust report; scikit-optimize trust report.