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A new version of Python package GPopt is available on PyPI. GPopt is a package for stochastic optimization based on Gaussian process regressors (for now, the name GP* is ‘unfortunate’). This type of optimization is particularly useful for tuning machine learning models’ hyperparameters.
The main change in GPopt’s v0.3.0 is: the user can now choose a different surrogate model (see this excellent book for more details on the procedure).
You’ll find below a link to a notebook showcasing the use of GPopt for tuning Boosted Configuration Networks (BCN version 0.7.0).
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