Automated machine learning.
Project description
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:
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 and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
booktitle = {Advances in Neural Information Processing Systems 28 (2015)},
pages = {2962--2970},
year = {2015}
}
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}
}
Also, have a look at the blog on automl.org where we regularly release blogposts.
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