auto-sklearn
auto-sklearn is free, open-source software that automates fitting machine-learning models and building ensembles for classification and regression.
What auto-sklearn is
auto-sklearn is free, open-source software that automates fitting machine-learning models and building ensembles for classification and regression. Auto-sklearn 2.0 chooses models, determines whether to use Successive Halving, and applies portfolios for meta-learning. Its fitted pipelines work with the scikit-learn API. You can set metrics, train-validation splits, and feature types, and inspect the search procedure. The system can also be extended with registered wrapper classes for classification, regression, and feature-preprocessing methods.
The project provides pip, conda-forge, source, and Docker installation options. It requires Linux: it cannot run on Windows because it relies on Python’s Unix-only resource module, and macOS is not actively supported due to resource-limit and dependency issues. You must set time and memory limits; it uses one CPU core by default. Parallel workers need a shared file system for training data and models. The free plan requires Python >=3.7 and a C++11 compiler. auto-sklearn is a project of the Chair for Machine Learning at the University of Freiburg.
Good
- Automates classification and regression model fitting.
- Fitted pipelines are compatible with the scikit-learn API.
- Metrics, splits, and feature types are customizable.
- Provides pip, conda-forge, source, and Docker installation options.
Watch out
- Cannot run on Windows.
- macOS is not actively supported.
- Requires set time and memory limits.
- Parallel workers require a shared file system.
Who it suits
auto-sklearn suits Linux users who want to automate classification or regression model fitting while retaining control over metrics, splits, and feature types. It may also suit teams able to provide shared storage for parallel workers.
Verdict
auto-sklearn offers automated model search and ensemble construction with scikit-learn-compatible pipelines and customization options. Its Linux requirement and resource setup are important constraints to consider.
Plans and prices
From auto-sklearn's own pricing page, checked 9 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Free open-source software | Free | Linux required · Python >=3.7 · C++11 compiler required |
The facts
Each one read from auto-sklearn's own pages, with the date it was checked.
What it does
- AutoML method
- Auto-sklearn 2.0 automatically selects models, decides whether to use Successive Halving, and uses portfolios for meta-learning.checked 9 Oct 2026
- Customization
- Users can customize metrics, train-validation splits, feature types, and inspect the search procedure.checked 9 Oct 2026
- Distributed runs
- Parallel auto-sklearn workers require access to a shared file system for training data and models.checked 9 Oct 2026
- Extensibility
- Users can add classification, regression, and feature-preprocessing methods by implementing and registering wrapper classes.checked 9 Oct 2026
- Install options
- The documentation describes pip, conda-forge, source, and Docker installation options.checked 9 Oct 2026
- Resource limits
- Users must set time and memory limits, and auto-sklearn uses one CPU core by default.checked 9 Oct 2026
- Scikit-learn compatibility
- Its fitted pipelines are compatible with the scikit-learn API.checked 9 Oct 2026
- Security
- The opened product documentation describes time and memory resource limits but does not state security certifications or compliance claims.checked 9 Oct 2026
- What it does
- auto-sklearn automates machine learning model fitting and ensemble construction for classification and regression tasks.checked 9 Oct 2026
Where it runs
- macOS support
- The project says it does not actively support macOS because of resource-limit and dependency issues.checked 9 Oct 2026
- Windows support
- The documentation says auto-sklearn cannot run on Windows because it relies on Python’s Unix-only resource module.checked 9 Oct 2026
The company
- Maker
- The AutoML website identifies AutoML as a project of the Chair for Machine Learning at the University of Freiburg.checked 9 Oct 2026
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