mlr3mbo-package {mlr3mbo}

mlr3mbo: Flexible Bayesian Optimization

Description

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) doi:10.1023/A:1008306431147, ParEGO by Knowles (2006) doi:10.1109/TEVC.2005.851274 and SMS-EGO by Ponweiser et al. (2008) doi:10.1007/978-3-540-87700-4_78.

Author(s)

Maintainer: Lennart Schneider [e-mail] (ORCID)

Authors:

• Jakob Richter [e-mail] (ORCID)

• Marc Becker [e-mail] (ORCID)

• Michel Lang [e-mail] (ORCID)

• Bernd Bischl [e-mail] (ORCID)

• Florian Pfisterer [e-mail] (ORCID)

• Martin Binder [e-mail]

• Sebastian Fischer [e-mail] (ORCID)

Other contributors:

• Michael H. Buselli [copyright holder]

• Wessel Dankers [copyright holder]

• Carlos Fonseca [copyright holder]

• Manuel Lopez-Ibanez [copyright holder]

• Luis Paquete [copyright holder]

See Also

Useful links:

• https://mlr3mbo.mlr-org.com

• https://github.com/mlr-org/mlr3mbo

• Report bugs at https://github.com/mlr-org/mlr3mbo/issues