Nothing
mlr3mbo: Flexible Bayesian Optimization
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>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Lennart Schneider [cre, aut] (<https://orcid.org/0000-0003-4152-5308>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Marc Becker [aut] (<https://orcid.org/0000-0002-8115-0400>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Florian Pfisterer [aut] (<https://orcid.org/0000-0001-8867-762X>), Martin Binder [aut], Sebastian Fischer [aut] (<https://orcid.org/0000-0002-9609-3197>), Michael H. Buselli [cph], Wessel Dankers [cph], Carlos Fonseca [cph], Manuel Lopez-Ibanez [cph], Luis Paquete [cph] |
| Maintainer | Lennart Schneider <lennart.sch@web.de> |
| License | LGPL-3 |
| Version | 0.2.6 |
| URL | https://mlr3mbo.mlr-org.com https://github.com/mlr-org/mlr3mbo |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the mlr3mbo package in your browser
Any scripts or data that you put into this service are public.
