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mlr3mbo
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.mlr3mbo . https://github.com/mlr-org/mlr3mbo/issues . https://mlr3mbo.mlr-org.com . https://github.com/mlr-org/mlr3mbo . mlr3mbo results . mlr3mbo.pdf . mlr3mbo_0.2.8.tar.gz . mlr3mbo_0.2.8.zip . mlr3mbo_0.2.8.zip . mlr3mbo_0.2.8.zip . mlr3mbo_0.2.8.tgz . mlr3mbo_0.2.8.tgz . mlr3mbo_0.2.8.tgz . mlr3mbo_0.2.8.tgz . mlr3mbo archive . https://CRAN.R-project.org/package=mlr3mbo .
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:
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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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