emoa, mlr3learners, mlr3pipelines, rpart, testthat (>=
3.0.0), xgboost

'TunerHyperband.R' 'TunerSuccessiveHalving.R'
'OptimizerHyperband.R' 'OptimizerSuccessiveHalving.R'
'nds_selection.R' 'bibentries.R' 'zzz.R'

'TunerHyperband.R' 'TunerSuccessiveHalving.R'
'OptimizerHyperband.R' 'OptimizerSuccessiveHalving.R'
'bibentries.R' 'helper.R' 'zzz.R'

emoa, mlr3learners (>= 0.5.2), mlr3pipelines, rpart, testthat
(>= 3.0.0), xgboost

c(
person("Marc", "Becker", , "marcbecker@posteo.de", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8115-0400")),
person("Sebastian", "Gruber", , "gruber_sebastian@t-online.de", role = "aut",
comment = c(ORCID = "0000-0002-8544-3470")),
person("Jakob", "Richter", , "jakob1richter@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-4481-5554")),
person("Julia", "Moosbauer", , "ju.moosbauer@googlemail.com", role = "aut",
comment = c(ORCID = "0000-0002-0000-9297")),
person("Bernd", "Bischl", , "bernd_bischl@gmx.net", role = "aut",
comment = c(ORCID = "0000-0001-6002-6980"))
)
Implements hyperband method for hyperparameter tuning.
Various termination criteria can be set and combined. The class
'AutoTuner' provides a convenient way to perform nested resampling in
combination with 'mlr3'. The hyperband algorithm was proposed by Lisha
Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh and Ameet
Talwalkar (2018) <arXiv:1603.06560>.

c(person(given = "Michel",
family = "Lang",
role = c("cre", "aut"),
email = "michellang@gmail.com",
comment = c(ORCID = "0000-0001-9754-0393")),
person(given = "Quay",
family = "Au",
role = "aut",
email = "quayau@gmail.com",
comment = c(ORCID = "0000-0002-5252-8902")),
person(given = "Stefan",
family = "Coors",
role = "aut",
email = "mail@stefancoors.de",
comment = c(ORCID = "0000-0002-7465-2146")))
Recommended Learners for 'mlr3'. Extends 'mlr3'
with interfaces to essential machine learning packages on CRAN.
This includes, but is not limited to: (penalized) linear and logistic
regression, linear and quadratic discriminant analysis, k-nearest neighbors,
naive Bayes, support vector machines, and gradient boosting.
data.table, mlr3 (>= 0.1.1), mlr3misc, paradox, R6
checkmate, DiceKriging, e1071, glmnet, kknn, lgr, MASS,
ranger, testthat, withr, xgboost