Comparison
auto-sklearn vs automl-gs
Verdict
Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data.
Markdown twin · auto-sklearn alternatives · automl-gs alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | automl-gs |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Dormant (2477d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · 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
- auto-sklearn
- Automated Machine Learning with scikit-learn
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
Stars
- auto-sklearn
- 8.1k
- automl-gs
- 1.9k
Forks
- auto-sklearn
- 1.3k
- automl-gs
- 181
Open issues
- auto-sklearn
- 209
- automl-gs
- 28
Language
- auto-sklearn
- Python
- automl-gs
- Python
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
Persona
- auto-sklearn
- -
- automl-gs
- -
Runtime
- auto-sklearn
- -
- automl-gs
- -
License
- auto-sklearn
- BSD-3-Clause
- automl-gs
- MIT
Last pushed
- auto-sklearn
- Jun 29, 2026
- automl-gs
- Oct 22, 2019
Categories
- auto-sklearn
- Model Training
- automl-gs
- Data & Retrieval, Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- automl-gs
- Dormant (18%)
Days since push
- auto-sklearn
- 35d
- automl-gs
- 2477d
Open issues (now)
- auto-sklearn
- 209
- automl-gs
- 28
Owner type
- auto-sklearn
- Organization
- automl-gs
- User
Full report
- auto-sklearn
- Trust report
- automl-gs
- Trust report
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, automl-gs is MIT.
- Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search.
- auto-sklearn ships Docker support for self-hosted deployment.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Choose automl-gs if…
- License: automl-gs is MIT, auto-sklearn is BSD-3-Clause.
- Tags unique to automl-gs: keras, machine-learning, 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/auto-sklearn) · observed Aug 4, 2026
- GitHub forks (automl/auto-sklearn) · observed Aug 4, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- 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 (minimaxir/automl-gs) · observed Aug 4, 2026
- GitHub forks (minimaxir/automl-gs) · observed Aug 4, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · automl-gs 1.9k (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and automl-gs?
- auto-sklearn: Automated Machine Learning with scikit-learn. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over automl-gs?
- Choose auto-sklearn over automl-gs when License: auto-sklearn is BSD-3-Clause, automl-gs is MIT; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, hyperparameter-search; auto-sklearn ships Docker support for self-hosted deployment; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- When should I choose automl-gs over auto-sklearn?
- Choose automl-gs over auto-sklearn when License: automl-gs is MIT, auto-sklearn is BSD-3-Clause; Tags unique to automl-gs: keras, machine-learning, python, tensorflow; Also covers Data & Retrieval; Need to rapidly prototype models with limited ML expertise.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- When should I avoid automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- Is auto-sklearn or automl-gs more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and automl-gs open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, automl-gs: MIT).
- Where can I find alternatives to auto-sklearn or automl-gs?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and automl-gs alternatives (auto-sklearn markdown twin, automl-gs 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, auto-sklearn or automl-gs?
- auto-sklearn: Steady. automl-gs: Dormant. 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 auto-sklearn and automl-gs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; automl-gs trust report.