# automl/auto-sklearn

Automated Machine Learning with scikit-learn

Repository: https://github.com/automl/auto-sklearn
Canonical: https://ross.abutalabs.com/products/auto-sklearn
Homepage: https://automl.github.io/auto-sklearn
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: automl, scikit-learn, automated-machine-learning, hyperparameter-optimization, hyperparameter-tuning, hyperparameter-search, bayesian-optimization, metalearning, meta-learning, smac
Last push: 2026-06-29T14:03:15+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 8, longevity 100
- inputs: {"age_days": 4080, "days_push": 65, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8126, forks 1324 (observed 2026-08-28T04:10:13.779611+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking
- domain: machine-learning, data-science, artificial-intelligence
- platform: python
- tags: automl, scikit-learn, hyperparameter-optimization, bayesian-optimization, meta-learning, smac, linux, macos

## Member repositories
- automl/auto-sklearn (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:13.779611+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:30:37.302717+00:00, confidence not recorded.
  - readme: https://github.com/automl/auto-sklearn (fetched 2026-08-28T04:10:13.779611+00:00, sha 77c630f9f733)
  - homepage: https://automl.github.io/auto-sklearn (fetched 2026-08-29T08:28:34.323040+00:00, sha 72d3eda9297d)
- Data as of 2026-08-30T08:39:29.467469+00:00.