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mlr3mbo  

Flexible Bayesian Optimization
View on CRAN: Click here


Download and install mlr3mbo package within the R console
Install from CRAN:
install.packages("mlr3mbo")

Install from Github:
library("remotes")
install_github("cran/mlr3mbo")

Install by package version:
library("remotes")
install_version("mlr3mbo", "0.2.8")



Attach the package and use:
library("mlr3mbo")
Maintained by
Lennart Schneider
[Scholar Profile | Author Map]
First Published: 2022-11-18
Latest Update: 2024-03-01
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) , ParEGO by Knowles (2006) and SMS-EGO by Ponweiser et al. (2008) .
How to cite:
Lennart Schneider (2022). mlr3mbo: Flexible Bayesian Optimization. R package version 0.2.8, https://cran.r-project.org/web/packages/mlr3mbo. Accessed 10 Jan. 2025.
Previous versions and publish date:
0.1.1 (2022-11-18 11:40), 0.1.2 (2023-03-03 01:20), 0.2.1 (2023-06-05 13:00), 0.2.2 (2024-03-01 20:40), 0.2.3 (2024-07-01 10:50), 0.2.4 (2024-07-06 19:10), 0.2.5 (2024-09-25 00:00), 0.2.6 (2024-10-16 19:30), 0.2.7 (2024-11-15 17:50)
Other packages that cited mlr3mbo R package
View mlr3mbo citation profile
Other R packages that mlr3mbo depends, imports, suggests or enhances
Complete documentation for mlr3mbo
Functions, R codes and Examples using the mlr3mbo R package
Some associated functions: AcqFunction . AcqOptimizer . ResultAssigner . Surrogate . SurrogateLearner . SurrogateLearnerCollection . acqf . acqo . default_acqfunction . default_acqoptimizer . default_loop_function . default_result_assigner . default_surrogate . loop_function . mbo_defaults . mlr3mbo-package . mlr_acqfunctions . mlr_acqfunctions_aei . mlr_acqfunctions_cb . mlr_acqfunctions_ehvi . mlr_acqfunctions_ehvigh . mlr_acqfunctions_ei . mlr_acqfunctions_eips . mlr_acqfunctions_mean . mlr_acqfunctions_pi . mlr_acqfunctions_sd . mlr_acqfunctions_smsego . mlr_loop_functions . mlr_loop_functions_ego . mlr_loop_functions_emo . mlr_loop_functions_mpcl . mlr_loop_functions_parego . mlr_loop_functions_smsego . mlr_optimizers_mbo . mlr_result_assigners . mlr_result_assigners_archive . mlr_result_assigners_surrogate . mlr_tuners_mbo . ras . srlrn . 
Some associated R codes: AcqFunction.R . AcqFunctionAEI.R . AcqFunctionCB.R . AcqFunctionEHVI.R . AcqFunctionEHVIGH.R . AcqFunctionEI.R . AcqFunctionEIPS.R . AcqFunctionMean.R . AcqFunctionPI.R . AcqFunctionSD.R . AcqFunctionSmsEgo.R . AcqOptimizer.R . OptimizerMbo.R . ResultAssigner.R . ResultAssignerArchive.R . ResultAssignerSurrogate.R . Surrogate.R . SurrogateLearner.R . SurrogateLearnerCollection.R . TunerMbo.R . aaa.R . bayesopt_ego.R . bayesopt_emo.R . bayesopt_mpcl.R . bayesopt_parego.R . bayesopt_smsego.R . bibentries.R . helper.R . loop_function.R . mbo_defaults.R . mlr_acqfunctions.R . mlr_loop_functions.R . mlr_result_assigners.R . sugar.R . zzz.R .  Full mlr3mbo package functions and examples
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