Auto-sklearn
by University of Freiburg (AutoML Group) · free · Last verified 2026-03-17
Auto-sklearn is an open-source AutoML toolkit built on scikit-learn. It leverages Bayesian optimization, meta-learning, and automated ensemble construction to find the best-performing machine learning pipeline for a given tabular dataset. It is a prominent tool in academic research for automated model selection.
https://automl.github.io/auto-sklearn ↗B
B—Above Average
Adoption: C+Quality: AFreshness: BCitations: AEngagement: F
Specifications
- License
- BSD 3-Clause
- Pricing
- free
- Capabilities
- Automated Machine Learning for Tabular Data, Bayesian Hyperparameter Optimization, Meta-learning for Warm-Starting Optimization, Automated Ensemble Construction from Model Libraries, Automatic Feature Preprocessing and Scaling, Support for both Classification and Regression Tasks, Resource Management (Time and Memory Limits), Parallel Execution using Dask and Joblib
- Integrations
- [object Object], [object Object], [object Object], [object Object]
- Use Cases
- [object Object], [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- SDK Languages
- python
- Deployment
- self-hosted
- Rate Limits
- N/A (open-source)
- Data Privacy
- Fully self-hosted; academic tool with no telemetry
- Tags
- automl, bayesian-optimization, scikit-learn, meta-learning, ensembling, tabular-data, python, open-source, hyperparameter-tuning, model-selection, machine-learning
- Added
- 2026-03-17
- Completeness
- 0.7%
Index Score
61.1Adoption
58
Quality
83
Freshness
65
Citations
85
Engagement
0