As you pointed out, it is all about a balance, and every feature has different tradeoffs. SigOpt was designed to unlock the power of Bayesian optimization for

As you pointed out, it is all about a balance, and every feature has different tradeoffs. SigOpt was designed to unlock the power of Bayesian optimization for anyone doing machine learning. We believe that you shouldn't need to be an expert and spend countless hours of administration to achieve great results for every model. We're wrapping an ensemble of the best Bayesian methods behind a simple interface [0] and constantly making improvements so that people can focus on designing features and their individual domain expertise, instead of needing to build and maintain their own hyperparameter optimization tools to see the benefit. For experts who want to spend a lot of time and effort customizing, administering, updating, and maintaining a hyperparameter tuning solution I would recommend forking one of the open source packages out there like spearmint [1] or MOE [2] (disclaimer, I wrote MOE while working at Yelp). [0]: https://sigopt.com/docs https://sigopt.com/docs [1]: https://github.com/JasperSnoek/spearmint https://github.com/JasperSnoek/spearmint [2]: https://github.com/Yelp/MOE https://github.com/Yelp/MOE