automl/auto-sklearn

Health v2 · maintenance only

Adoption not part of the score

8126 stars · 1324 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

auto-sklearn is an automated machine learning (AutoML) toolkit that acts as a drop-in replacement for scikit-learn estimators, automatically searching for the best model and hyperparameters. It uses Bayesian optimization, meta-learning, and ensemble building to deliver strong out-of-the-box performance for classification and regression tasks.

Use cases

• automatically find the best machine learning model for my dataset
• tune hyperparameters without manual grid search
• replace a scikit-learn estimator with an AutoML version
• build an ensemble of models automatically for classification
• run automated machine learning on tabular data
• compare AutoML approaches for a research paper

When to choose

• you want a drop-in scikit-learn-compatible AutoML estimator
• you work with tabular classification or regression tasks
• you want meta-learning and ensemble construction built in
• you need a well-established, research-backed AutoML tool

When to avoid

• you need deep learning or neural architecture search
• you work primarily with images, text, or audio rather than tabular data
• you need active development or support for the latest Python versions
• you want a framework-agnostic AutoML beyond the scikit-learn ecosystem

Facets

library · maturity maintenance

machine-learning benchmarking machine-learning data-science artificial-intelligence python automl scikit-learn hyperparameter-optimization bayesian-optimization meta-learning smac linux macos

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