TLDR
Optuna is slightly better because of the flexibility, imperative approach to sampling parameters and a bit less boilerplate. Hyperopt has a ton of sampling options for each hyperparameter type (Float, integer, Categorical). Optuna has a search space definition, flexibility in defining a complex space and sampling options (Float) for each parameter type. In this article I will show you an example of using Optuna and Hyperopt on a real problem, compare them on API, documentation, functionality, and more, give you my overall score and recommendation on which library you should use.via the TL;DR App
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Written by neptuneAI_jakub | Senior data scientist building experiment tracking tools for ML projects at https://neptune.ai
