Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multipoint batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.

Maintainer: Jakob Richter <code at jakob-r.de>

Author(s): Bernd Bischl*, Jakob Bossek*, Jakob Richter*, Daniel Horn*, Michel Lang*, Janek Thomas*

Install package and any missing dependencies by running this line in your R console:

install.packages("mlrMBO")

Depends mlr(>=2.10), ParamHelpers(>=1.10), smoof(>=1.4)
Imports backports, BBmisc(>=1.11), checkmate(>=1.8.2), data.table, lhs, parallelMap(>=1.3)
Suggests akima, cmaesr(>=1.0.3), ggplot2, RColorBrewer, DiceKriging, DiceOptim, earth, emoa, GGally, gridExtra, kernlab, kknn, knitr, mco, nnet, party, randomForest, rmarkdown, rpart, testthat, eaf, covr
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Package mlrMBO
Materials
URL https://github.com/mlr-org/mlrMBO
Task Views
Version 1.0.0
Published 2017-03-12
License BSD_2_clause + file LICENSE
BugReports https://github.com/mlr-org/mlrMBO/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks mlrMBO check results
Package source mlrMBO_1.0.0.tar.gz