auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Learn more about the technology behind auto-sklearn by reading our paper published at NIPS 2015 . hat)) This will run for one hour and should result in an accuracy above 0.98.

License auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license. Citing auto-sklearn If you use auto-sklearn in a scientific publication, we would appreciate a reference to the following paper: Efficient and Robust Automated Machine Learning, Feurer et al., Advances in Neural Information Processing Systems 28 (NIPS 2015).

Bibtex entry: @incollection{NIPS2015_5872, 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}, editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett}, pages = {2962--2970}, year = {2015}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf } }

Contributing We appreciate all contribution to auto-sklearn, from bug reports and documentation to new features. If you want to contribute to the code, you can pick an issue from the issue tracker which is marked with Needs contributer.