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Comparison

evalml vs auto-sklearn

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.

Markdown twin · evalml alternatives · auto-sklearn alternatives

GraphCanon updated Sep 30, 2026

11views this month

evalml logo

evalml

alteryx/evalml

★ 850pushed Jan 14, 2026
vs
auto-sklearn logo

auto-sklearn

automl/auto-sklearn

★ 8.1kpushed Sep 15, 2026

Trust & integrity

Signalevalmlauto-sklearn
Maintenance
Slowing (258d since push)
As of Sep 30, 2026 · github_public_v1
Active (14d since push)
As of Sep 30, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 30, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 30, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Jul 11, 2026 · 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
evalml trust reportauto-sklearn trust reportTrust methodology

Tagline

evalml
An AutoML library written in Python
auto-sklearn
Automated Machine Learning with scikit-learn

Stars

evalml
850
auto-sklearn
8.1k

Forks

evalml
96
auto-sklearn
1.3k

Open issues

evalml
325
auto-sklearn
209

Language

evalml
Python
auto-sklearn
Python

Adopt for

evalml
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
auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.

Persona

evalml
-
auto-sklearn
-

Runtime

evalml
-
auto-sklearn
-

License

evalml
EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
auto-sklearn
BSD-3-Clause

Last pushed

evalml
Jan 14, 2026
auto-sklearn
Sep 15, 2026

Categories

evalml
Evaluation & Observability, Model Training
auto-sklearn
Model Training

Trust and health

Maintenance

evalml
Slowing (36%)
auto-sklearn
Active (82%)

Days since push

evalml
258d
auto-sklearn
14d

Open issues (now)

evalml
325
auto-sklearn
209

Stars delta

evalml
-2 (30d)
auto-sklearn
+7 (30d)

Open issues delta

evalml
+1 (30d)
auto-sklearn
0 (30d)

OSV dependency advisories

evalml
No lockfile (source not queried)
auto-sklearn
Published findings

Full report

evalml
Trust report
auto-sklearn
Trust report

Shared compatibility

  • Python · evalml: Python runtime · auto-sklearn: Python runtime

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.

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.
GitHubDetails

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 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.
GitHubDetails

Explore

evalml trust report →auto-sklearn trust report →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: evalml 850 · auto-sklearn 8.1k (synced Sep 30, 2026).

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 and auto-sklearn alternatives (evalml 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, 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; auto-sklearn trust report.

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