utopiazh / auto-sklearn

Automated Machine Learning with scikit-learn

auto-sklearn

auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

Find the documentation here. Quick links:

• Installation Guide
• Releases
• Manual
• Examples
• API

auto-sklearn in one image

auto-sklearn in four lines of code

import autosklearn.classification
cls = autosklearn.classification.AutoSklearnClassifier()
cls.fit(X_train, y_train)
predictions = cls.predict(X_test)

Relevant publications

If you use auto-sklearn in scientific publications, we would appreciate citations.

Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter
Advances in Neural Information Processing Systems 28 (2015)

Link to publication.

@inproceedings{feurer-neurips15a,
    title     = {Efficient and Robust Automated Machine Learning},
    author    = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina  Springenberg, Jost and Blum, Manuel and Hutter, Frank},
    booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
    pages     = {2962--2970},
    year      = {2015}
}

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Auto-Sklearn 2.0: The Next Generation
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter*
arXiv:2007.04074 [cs.LG], 2020

Link to publication.

@article{feurer-arxiv20a,
    title     = {Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning},
    author    = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank},
    booktitle = {arXiv:2007.04074 [cs.LG]},
    year      = {2020}
}

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Also, have a look at the blog on automl.org where we regularly release blogposts.