- Home
- Predictive Analytics Software
- auto-sklearn
auto-sklearn
What it is
auto-sklearn is free, open-source software that automates model fitting and ensemble construction for classification and regression. In version 2.0, it selects models, decides whether to use Successive Halving and applies portfolios for meta-learning. Fitted pipelines are compatible with the scikit-learn API. Users can customize metrics, train-validation splits and feature types, inspect the search procedure, and extend the tool by registering wrapper classes for classification, regression and feature preprocessing. Installation options include pip, conda-forge, source and Docker. The project requires Linux: it cannot run on Windows because it depends on Python’s Unix-only resource module, and macOS is not actively supported because of resource-limit and dependency issues. Users must set time and memory limits, and the default uses one CPU core. Parallel workers need a shared file system for training data and models. Its free open-source plan lists Linux, Python >=3.7 and a C++11 compiler as requirements. The AutoML website identifies the project with the Chair for Machine Learning at the University of Freiburg.
For and against
For
- Automates fitting for classification and regression.
- Pipelines work with the scikit-learn API.
- Users can customize metrics and data splits.
- Supports registered methods for added extensibility.
- Free open-source software.
Against
- Cannot run on Windows.
- macOS is not actively supported.
- Time and memory limits must be set.
- Parallel workers need a shared file system.
The verdict
auto-sklearn offers an adjustable AutoML workflow for Linux users, with scikit-learn-compatible pipelines. Check the operating-system and resource requirements before choosing it.
Who it suits
It suits people building classification or regression models on Linux who want automated fitting and ensemble construction. It may also suit users who want to adjust search settings or register their own methods.
Plans and prices
Free open-source software
Linux required · Python >=3.7 · C++11 compiler required
Read 9 Oct 2026 · automl.github.io
On the record
- AutoML method
- Auto-sklearn 2.0 automatically selects models, decides whether to use Successive Halving, and uses portfolios for meta-learning.automl.github.io · read 9 Oct 2026
- Customization
- Users can customize metrics, train-validation splits, feature types, and inspect the search procedure.automl.github.io · read 9 Oct 2026
- Distributed runs
- Parallel auto-sklearn workers require access to a shared file system for training data and models.automl.github.io · read 9 Oct 2026
- Extensibility
- Users can add classification, regression, and feature-preprocessing methods by implementing and registering wrapper classes.automl.github.io · read 9 Oct 2026
- Install options
- The documentation describes pip, conda-forge, source, and Docker installation options.automl.github.io · read 9 Oct 2026
- macOS support
- The project says it does not actively support macOS because of resource-limit and dependency issues.automl.github.io · read 9 Oct 2026
- Maker
- The AutoML website identifies AutoML as a project of the Chair for Machine Learning at the University of Freiburg.automl.org · read 9 Oct 2026
- Resource limits
- Users must set time and memory limits, and auto-sklearn uses one CPU core by default.automl.github.io · read 9 Oct 2026
- Scikit-learn compatibility
- Its fitted pipelines are compatible with the scikit-learn API.automl.github.io · read 9 Oct 2026
- Security
- The opened product documentation describes time and memory resource limits but does not state security certifications or compliance claims.automl.github.io · read 9 Oct 2026
- What it does
- auto-sklearn automates machine learning model fitting and ensemble construction for classification and regression tasks.automl.github.io · read 9 Oct 2026
- Windows support
- The documentation says auto-sklearn cannot run on Windows because it relies on Python’s Unix-only resource module.automl.github.io · read 9 Oct 2026
