Hyperparameters optimisation frameworks for R (such as Optuna or Hyperopt in Python)?

Hi Folks! Is anyone of you aware of such for R and could make any recommendations?

There are several hits for hyperparameter optimization on the R-tuned google front-end, including at least one for Bayesian.

Also browse through the CRAN task views

tune has basic Bayesian optimization built in.


Fresh off the presses - https://datascienceplus.com/grid-search-and-bayesian-hyperparameter-optimization-using-tune-and-caret-packages/

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Yes, I know and used tune with Bayesian optimization but in this particular case it wouldn't work I think (perhaps I should have elaborated more). I'm working with LightGbm with a particular time-series data structure and I don't think tune/ caret can be flexibly used in such case without converting the model to a parsnip specific format right?

On the other hand Optuna is generic/ framework agnostic - do you know of anything like that for R?

What about the package ParBayesianOptimization ?

bayesOpt: Bayesian Optimization with Gaussian Processes.
This package is less complex than the tune package.

Thanks for that - this looks quite promising!

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