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Decision brief
auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
Good fit when
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- For scenarios where integration with scikit-learn ecosystem is crucial, as auto-sklearn extends familiar APIs and methods within this environment.
Avoid when
- 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.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (35d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- 22 low (22 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install auto-sklearnHow it fits your stack(1)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
auto-sklearn is an automated machine learning toolkit that operates as a drop-in replacement for a scikit-learn estimator and focuses on automating the process of hyperparameter optimization.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 4, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 4, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
```python import autosklearn.classificationSource link
Tags
README
auto-sklearn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Find the documentation here. Quick links:
auto-sklearn in one image
auto-sklearn in four lines of code
import autosklearn.classification
cls = autosklearn.classification.AutoSklearnClassifier()
cls.fit(X_train, y_train)
predictions = cls.predict(X_test)
Relevant publications
If you use auto-sklearn in scientific publications, we would appreciate citations.
Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter Advances in Neural Information Processing Systems 28 (2015)
Link to publication.
@inproceedings{feurer-neurips15a,
title = {Efficient and Robust Automated Machine Learning},
author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
pages = {2962--2970},
year = {2015}
}
Auto-Sklearn 2.0: The Next Generation Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter* arXiv:2007.04074 [cs.LG], 2020
Link to publication.
@article{feurer-arxiv20a,
title = {Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning},
author = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank},
booktitle = {arXiv:2007.04074 [cs.LG]},
year = {2020}
}
Also, have a look at the blog on automl.org where we regularly release blogposts.
For agents
This page has a .md twin and JSON over the API.