mlr3tuning-package: mlr3tuning: Hyperparameter Optimization for 'mlr3'

In mlr3tuning: Hyperparameter Optimization for 'mlr3'

mlr3tuning: Hyperparameter Optimization for 'mlr3'

Description

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.

Author(s)

Maintainer: Marc Becker [e-mail] (ORCID)

Authors:

• Michel Lang [e-mail] (ORCID)

• Jakob Richter [e-mail] (ORCID)

• Bernd Bischl [e-mail] (ORCID)

• Daniel Schalk [e-mail] (ORCID)

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

mlr3tuning documentation built on Nov. 21, 2023, 1:06 a.m.