![]() We've released a new version with lots of new features and stability fixes. Our paper on Auto-WEKA 2.0 was accepted for publication at the Journal of Machine Learning Research, open source software track. Hyperparameter settings appropriate to their applications, and hence to Users to more effectively identify machine learning algorithms and ![]() Our hope is that Auto-WEKA will help non-expert ![]() Using a fully automated approach, leveraging recent innovations inīayesian optimization. Methods that address these issues in isolation. There are a staggeringly large number of possible alternatives overall.Īuto-WEKA considers the problem of simultaneously selecting a learningĪlgorithm and setting its hyperparameters, going beyond previous Hyperparameters that can drastically change their performance, and However, each of these algorithms have their own Many different machine learning algorithms exist thatĬan easily be used off the shelf, many of these methods are implemented
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