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auto-sklearn

Automated Machine Learning with scikit-learn

Our verdict

Not reviewed yet

We have not reviewed auto-sklearn yet

No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.

How we review records

From the listing

About auto-sklearn

auto-sklearn is an automated machine learning toolkit that extends scikit-learn with automatic model selection and hyperparameter optimization. It uses Bayesian optimization and meta-learning to efficiently search the space of scikit-learn classifiers and regressors, then builds an ensemble of the best performing models.

Best for

Data scientists and ML engineers who need an automated baseline for tabular classification and regression tasks using scikit-learn

Use cases

  • Quickly finding a strong baseline classifier or regressor on a new tabular dataset
  • Automating hyperparameter tuning for scikit-learn pipelines in research or production
  • Comparing AutoML performance against manually tuned scikit-learn models

Notes

8,102 stars on GitHub. Last updated 2026-04-21. Licensed BSD-3-Clause.

Pros

  • Proven AutoML algorithm with strong benchmark results on tabular data
  • Seamless integration with the scikit-learn ecosystem and its API
  • Meta-learning warm-starts the search using prior dataset performance

Cons

  • Can be computationally expensive and slow for large datasets without sufficient resources
  • Limited to scikit-learn estimators; no support for deep learning or gradient boosting outside XGBoost/LightGBM wrappers
  • Ensembling step increases model size and prediction latency

Key facts

What we have checked

Latest version
0.15.0 (released 13 Feb 2023)
Checked GitHub
Days since the last commit
52
Checked GitHub
GitHub forks
1,322
Checked GitHub
GitHub stars
8,149
Checked GitHub
Days to a first reply on issues
190.8
Checked GitHub
Open issues
200
Checked GitHub
Releases in the last 90 days
0
Checked GitHub

Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.

Stars and usage

How auto-sklearn is growing

GitHub stars

8,102 to 8,149

stars. 12 July 2026 to 6 Nov 2026, 4 readings.

Source: GitHub. Checked .

Health and security

Is auto-sklearn looked after, and what do its setup files show

Health score

54 out of 100

Does someone look after this repository? It does not say the code is good or safe. Worked out from GitHub signals checked . How it is worked out

PartPointsWhat we saw
Recent activity28 of 30Last push 52 days ago
Releases0 of 150 releases in 90 days
Issue replies0 of 20First reply in 190.8 days (median)
Maintainer15 of 15Owned by an organisation
Official sourceNot countedMCP servers only

Sources

Where this comes from

  • GitHubOfficial

    Checked · Latest version

  • GitHubRegistry

    Checked · Days since the last commit, GitHub forks, GitHub stars, Days to a first reply on issues, Open issues, Releases in the last 90 days

A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.

Changelog

What changed

  1. Released 0.15.0

    Source: GitHub

  2. Released 0.14.7

    Source: GitHub

  3. Released 0.14.6

    Source: GitHub

  4. Released 0.14.5

    Source: GitHub

  5. Released 0.14.4

    Source: GitHub

  6. Released 0.14.3

    Source: GitHub

  7. Released 0.14.1

    Source: GitHub

  8. Released 0.14.0

    Source: GitHub

  9. Released 0.13.0

    Source: GitHub

  10. Released 0.12.7

    Source: GitHub

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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