Comparison
automl-gs vs scikit-optimize
Verdict
Pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data; 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 · automl-gs alternatives · scikit-optimize alternatives
GraphCanon updated Oct 3, 2026
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Trust & integrity
| Signal | automl-gs | scikit-optimize |
|---|---|---|
| Maintenance | Dormant (2534d since push) As of Sep 30, 2026 · github_public_v1 | Archived (952d since push) As of Oct 3, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
- scikit-optimize
- Sequential model-based optimization library with scipy.optimize interface
Stars
- automl-gs
- 1.9k
- scikit-optimize
- 2.8k
Forks
- automl-gs
- 178
- scikit-optimize
- 555
Open issues
- automl-gs
- 28
- scikit-optimize
- 318
Language
- automl-gs
- Python
- scikit-optimize
- Python
Adopt for
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
- 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
- automl-gs
- -
- scikit-optimize
- -
Runtime
- automl-gs
- -
- scikit-optimize
- -
License
- automl-gs
- MIT
- scikit-optimize
- BSD-3-Clause
Last pushed
- automl-gs
- Oct 22, 2019
- scikit-optimize
- Feb 23, 2024
Categories
- automl-gs
- Data & Retrieval, Model Training
- scikit-optimize
- Model Training
Trust and health
Maintenance
- automl-gs
- Dormant (18%)
- scikit-optimize
- Archived (8%)
Days since push
- automl-gs
- 2534d
- scikit-optimize
- 952d
Archived on GitHub
- automl-gs
- No
- scikit-optimize
- Yes
Open issues (now)
- automl-gs
- 28
- scikit-optimize
- 318
Stars delta
- automl-gs
- -2 (30d)
- scikit-optimize
- +1 (30d)
Owner type
- automl-gs
- User
- scikit-optimize
- Organization
Full report
- automl-gs
- Trust report
- scikit-optimize
- Trust report
Choose automl-gs if…
- License: automl-gs is MIT, scikit-optimize is BSD-3-Clause.
- Tags unique to automl-gs: automl, keras, python, tensorflow.
- Also covers Data & Retrieval.
- Need to rapidly prototype models with limited ML expertise
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
Choose scikit-optimize if…
- License: scikit-optimize is BSD-3-Clause, automl-gs is MIT.
- Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, scikit-learn.
- Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.
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 (minimaxir/automl-gs) · observed Sep 30, 2026
- GitHub forks (minimaxir/automl-gs) · observed Sep 30, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (scikit-optimize/scikit-optimize) · observed Oct 3, 2026
- GitHub forks (scikit-optimize/scikit-optimize) · observed Oct 3, 2026
- Last push (scikit-optimize/scikit-optimize) · observed Feb 23, 2024
- License file (BSD-3-Clause) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: automl-gs 1.9k · scikit-optimize 2.8k (synced Sep 30, 2026).
Common questions
- What is the difference between automl-gs and scikit-optimize?
- automl-gs: Automatically generate machine-learning models and code with input CSV and target field. 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 automl-gs over scikit-optimize?
- Choose automl-gs over scikit-optimize when License: automl-gs is MIT, scikit-optimize is BSD-3-Clause; Tags unique to automl-gs: automl, keras, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.
- When should I choose scikit-optimize over automl-gs?
- Choose scikit-optimize over automl-gs when License: scikit-optimize is BSD-3-Clause, automl-gs is MIT; Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, scikit-learn; Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.
- When should I avoid automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- 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 automl-gs or scikit-optimize more popular on GitHub?
- scikit-optimize has more GitHub stars (2,830 vs 1,867). Stars measure visibility, not whether either tool fits your constraints.
- Are automl-gs and scikit-optimize open source?
- Yes - both are open-source projects on GitHub (automl-gs: MIT, scikit-optimize: BSD-3-Clause).
- Where can I find alternatives to automl-gs or scikit-optimize?
- GraphCanon lists graph-backed alternatives at automl-gs alternatives and scikit-optimize alternatives (automl-gs 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, automl-gs or scikit-optimize?
- automl-gs: 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 automl-gs and scikit-optimize?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: automl-gs trust report; scikit-optimize trust report.