mlr3tuning: Tuning for 'mlr3'

Hyperparameter optimization package of the mlr3 ecosystem. It features highly configurable search spaces via the paradox package and finds optimal hyperparameter configurations for any mlr3 learner. mlr3tuning works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in mlr3mbo) and Hyperband (in mlr3hyperband). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.

See also

Useful links:

• https://mlr3tuning.mlr-org.com

• https://github.com/mlr-org/mlr3tuning

• Report bugs at https://github.com/mlr-org/mlr3tuning/issues

Author

Maintainer: Marc Becker marcbecker@posteo.de (ORCID)

Authors:

• Michel Lang michellang@gmail.com (ORCID)

• Jakob Richter jakob1richter@gmail.com (ORCID)

• Bernd Bischl bernd_bischl@gmx.net (ORCID)

• Daniel Schalk daniel.schalk@stat.uni-muenchen.de (ORCID)