--- title: "FEDOT vs auto-sklearn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-automl-auto-sklearn" tools: ["aimclub-fedot", "automl-auto-sklearn"] --- # FEDOT vs auto-sklearn *GraphCanon updated Aug 4, 2026* ## 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. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [auto-sklearn](https://automl.github.io/auto-sklearn) has 8.1k stars, 1.3k forks, and 209 open issues, last pushed Jun 29, 2026. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [auto-sklearn's repository](https://github.com/automl/auto-sklearn). | | [FEDOT](/tools/aimclub-fedot.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Automated Machine Learning with scikit-learn | | Stars | 709 | 8,127 | | Forks | 94 | 1,327 | | Open issues | 82 | 209 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | BSD-3-Clause | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 7d | 35d | | Open issues (now) | 82 | 209 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/automl-auto-sklearn/trust.md) | ## Shared compatibility - **Python**: [FEDOT](/tools/aimclub-fedot.md) - Python runtime; [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime ## Decision facts: FEDOT - **Adopt for:** FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. ## Decision facts: auto-sklearn - **Adopt for:** auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. ## Choose when ### 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). ### 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 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 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. ## 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](/tools/aimclub-fedot/alternatives) and [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [auto-sklearn markdown twin](/tools/automl-auto-sklearn/alternatives.md)), 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](/compare/aimclub-fedot-vs-automl-auto-sklearn.md) 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](/tools/aimclub-fedot/trust); [auto-sklearn trust report](/tools/automl-auto-sklearn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimclub-fedot`](/api/graphcanon/graph?tool=aimclub-fedot) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_