Comparison
auto-sklearn vs autokeras
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 autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
Markdown twin · auto-sklearn alternatives · autokeras alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | autokeras |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Slowing (251d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) 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
- autokeras
- AutoML library for deep learning
Stars
- auto-sklearn
- 8.1k
- autokeras
- 9.3k
Forks
- auto-sklearn
- 1.3k
- autokeras
- 1.4k
Open issues
- auto-sklearn
- 209
- autokeras
- 161
Language
- auto-sklearn
- Python
- autokeras
- 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.
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
Persona
- auto-sklearn
- -
- autokeras
- -
Runtime
- auto-sklearn
- -
- autokeras
- -
License
- auto-sklearn
- BSD-3-Clause
- autokeras
- Apache-2.0
Last pushed
- auto-sklearn
- Jun 29, 2026
- autokeras
- Nov 25, 2025
Categories
- auto-sklearn
- Model Training
- autokeras
- Developer Tools, Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- autokeras
- Slowing (36%)
Days since push
- auto-sklearn
- 35d
- autokeras
- 251d
Open issues (now)
- auto-sklearn
- 209
- autokeras
- 161
OSV dependency advisories
- auto-sklearn
- Published findings
- autokeras
- No lockfile (source not queried)
Full report
- auto-sklearn
- Trust report
- autokeras
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · autokeras: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, autokeras is Apache-2.0.
- 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 autokeras if…
- License: autokeras is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to autokeras: autodl, deep-learning, keras, machine-learning.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When NOT to use autokeras
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
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 (keras-team/autokeras) · observed Aug 4, 2026
- GitHub forks (keras-team/autokeras) · observed Aug 4, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 2025
- License file (Apache-2.0) · 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 · autokeras 9.3k (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and autokeras?
- auto-sklearn: Automated Machine Learning with scikit-learn. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over autokeras?
- Choose auto-sklearn over autokeras when License: auto-sklearn is BSD-3-Clause, autokeras is Apache-2.0; 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 autokeras over auto-sklearn?
- Choose autokeras over auto-sklearn when License: autokeras is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- 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 autokeras?
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
- Is auto-sklearn or autokeras more popular on GitHub?
- autokeras has more GitHub stars (9,328 vs 8,127). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and autokeras open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, autokeras: Apache-2.0).
- Where can I find alternatives to auto-sklearn or autokeras?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and autokeras alternatives (auto-sklearn markdown twin, autokeras 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 autokeras?
- auto-sklearn: Steady. autokeras: Slowing. 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 autokeras?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; autokeras trust report.