--- title: "evalml vs auto-sklearn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-automl-auto-sklearn" tools: ["alteryx-evalml", "automl-auto-sklearn"] --- # evalml vs auto-sklearn *GraphCanon updated Sep 30, 2026* ## Verdict Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. [evalml](https://evalml.alteryx.com) reports 850 GitHub stars, 96 forks, and 325 open issues, last pushed Jan 14, 2026. [auto-sklearn](https://automl.github.io/auto-sklearn) has 8.1k stars, 1.3k forks, and 209 open issues, last pushed Sep 15, 2026. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [auto-sklearn's repository](https://github.com/automl/auto-sklearn). | | [evalml](/tools/alteryx-evalml.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | Automated Machine Learning with scikit-learn | | Stars | 850 | 8,134 | | Forks | 96 | 1,324 | | Open issues | 325 | 209 | | Language | Python | Python | | Adopt for | EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. | auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | BSD-3-Clause | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [auto-sklearn](/tools/automl-auto-sklearn.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 258d | 14d | | Open issues (now) | 325 | 209 | | Stars delta | -2 (30d) | +7 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/automl-auto-sklearn/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [auto-sklearn](/tools/automl-auto-sklearn.md) - Python runtime ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## 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 evalml if… - Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.. - Requirements: Min 2 GB RAM. - Tags unique to evalml: data-science, feature-engineering, feature-selection, machine-learning. - Also covers Evaluation & Observability. - You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### Choose auto-sklearn if… - 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 evalml - You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. - Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ## 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 evalml and auto-sklearn? evalml: An AutoML library written in Python. auto-sklearn: Automated Machine Learning with scikit-learn. See the comparison table for live GitHub stats and shared categories. ### When should I choose evalml over auto-sklearn? Choose evalml over auto-sklearn when Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: data-science, feature-engineering, feature-selection, machine-learning; Also covers Evaluation & Observability; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### When should I choose auto-sklearn over evalml? Choose auto-sklearn over evalml when 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 avoid evalml? You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ### 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 evalml or auto-sklearn more popular on GitHub? auto-sklearn has more GitHub stars (8,134 vs 850). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and auto-sklearn open source? Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, auto-sklearn: BSD-3-Clause). ### Where can I find alternatives to evalml or auto-sklearn? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [auto-sklearn alternatives](/tools/automl-auto-sklearn/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/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/alteryx-evalml-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, evalml or auto-sklearn? evalml: Slowing. auto-sklearn: Active. 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 evalml and auto-sklearn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [auto-sklearn trust report](/tools/automl-auto-sklearn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - 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/_