Comparison
FEDOT vs auto-sklearn
Verdict
Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
Markdown twin · FEDOT alternatives · auto-sklearn alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FEDOT | auto-sklearn |
|---|---|---|
| Maintenance | Active (7d since push) As of 2w · github_public_v1 | Steady (35d 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
- FEDOT
- Automated modeling and machine learning framework FEDOT
- auto-sklearn
- Automated Machine Learning with scikit-learn
Stars
- FEDOT
- 709
- auto-sklearn
- 8.1k
Forks
- FEDOT
- 94
- auto-sklearn
- 1.3k
Open issues
- FEDOT
- 82
- auto-sklearn
- 209
Language
- FEDOT
- Python
- auto-sklearn
- Python
Adopt for
- FEDOT
- FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
Persona
- FEDOT
- -
- auto-sklearn
- -
Runtime
- FEDOT
- -
- auto-sklearn
- -
License
- FEDOT
- BSD-3-Clause
- auto-sklearn
- BSD-3-Clause
Last pushed
- FEDOT
- Jul 27, 2026
- auto-sklearn
- Jun 29, 2026
Categories
- FEDOT
- Model Training
- auto-sklearn
- Model Training
Trust and health
Maintenance
- FEDOT
- Active (82%)
- auto-sklearn
- Steady (60%)
Days since push
- FEDOT
- 7d
- auto-sklearn
- 35d
Open issues (now)
- FEDOT
- 82
- auto-sklearn
- 209
Full report
- FEDOT
- Trust report
- auto-sklearn
- Trust report
Shared compatibility
- Python · FEDOT: Python runtime · auto-sklearn: Python runtime
Choose FEDOT if…
- Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, structural-learning.
- For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types
- More recently updated (last pushed Jul 27, 2026).
When NOT to use FEDOT
- In scenarios with strict real-time requirements due to its optimization time
- For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs
- When the project specifically requires a non-evolutionary approach for pipeline design
Choose auto-sklearn if…
- Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-search, hyperparameter-tuning.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aimclub/FEDOT) · observed Aug 4, 2026
- GitHub forks (aimclub/FEDOT) · observed Aug 4, 2026
- Last push (aimclub/FEDOT) · observed Jul 27, 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 (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 on cards: FEDOT 709 · auto-sklearn 8.1k (synced Aug 4, 2026).
Common questions
- What is the difference between FEDOT and auto-sklearn?
- FEDOT: Automated modeling and machine learning framework FEDOT. auto-sklearn: Automated Machine Learning with scikit-learn. See the comparison table for live GitHub stats and shared categories.
- When should I choose FEDOT over auto-sklearn?
- Choose FEDOT over auto-sklearn when Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, structural-learning; For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types; More recently updated (last pushed Jul 27, 2026).
- When should I choose auto-sklearn over FEDOT?
- Choose auto-sklearn over FEDOT when Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-search, hyperparameter-tuning; 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 avoid FEDOT?
- In scenarios with strict real-time requirements due to its optimization time For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs When the project specifically requires a non-evolutionary approach for pipeline design
- 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.
- Is FEDOT or auto-sklearn more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 709). Stars measure visibility, not whether either tool fits your constraints.
- Are FEDOT and auto-sklearn open source?
- Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, auto-sklearn: BSD-3-Clause).
- Where can I find alternatives to FEDOT or auto-sklearn?
- GraphCanon lists graph-backed alternatives at FEDOT alternatives and auto-sklearn alternatives (FEDOT markdown twin, auto-sklearn 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, FEDOT or auto-sklearn?
- FEDOT: Active. auto-sklearn: Steady. 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 FEDOT and auto-sklearn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FEDOT trust report; auto-sklearn trust report.