auto-sklearn

Overview

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

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

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.

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

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

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

Indexed from awesome-llmops and enriched against its public facts.