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Many different machine learning algorithms exist that can easily be used off the shelf, many of these methods are implemented in the open source WEKA package. However, each of these algorithms have their own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall. Auto-WEKA considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous methods that address these issues in isolation. Auto-WEKA does this using a fully automated approach, leveraging recent innovations in Bayesian optimization. Our hope is that Auto-WEKA will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.

Latest news

29 November, 2016 Our paper on Auto-WEKA 2.0 was accepted for publication at the Journal of Machine Learning Research, open source software track.
10 November, 2016 We've released a new version with lots of new features and stability fixes.
06 July, 2016 We've updated the WEKA version, support returning more than one configuration and fixed a few bugs.
May, 2016 Auto-WEKA is back with a vengeance! We have just released version 2.0 with a new interface that integrates into WEKA's package manager.
12 November, 2013 Bug fixes, updated documentation, more usability tweaks
9 August, 2013 New version of Auto-WEKA code, emphasis on usability
5 June, 2013 Added datasets, updated Auto-WEKA code
2 March, 2013 Initial release of Auto-WEKA





Auto-WEKA is released under the GNU General Public License version 3.
There is the similarly-named AutoWEKA project. We are in no way affiliated with each other and indeed were not aware of its existence until very recently.

Please send any questions, concerns or comments to Lars Kotthoff.