View source: R/TuneControlMBO.R
makeTuneControlMBO | R Documentation |
Model-based / Bayesian optimization with the function mlrMBO::mbo from the mlrMBO package. Please refer to https://github.com/mlr-org/mlrMBO for further info.
makeTuneControlMBO( same.resampling.instance = TRUE, impute.val = NULL, learner = NULL, mbo.control = NULL, tune.threshold = FALSE, tune.threshold.args = list(), continue = FALSE, log.fun = "default", final.dw.perc = NULL, budget = NULL, mbo.design = NULL )
same.resampling.instance |
( |
impute.val |
(numeric) |
learner |
(Learner | |
mbo.control |
(mlrMBO::MBOControl | |
tune.threshold |
( |
tune.threshold.args |
(list) |
continue |
( |
log.fun |
( |
final.dw.perc |
( |
budget |
( |
mbo.design |
(data.frame | |
(TuneControlMBO)
Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas and Michel Lang; mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions, Preprint: https://arxiv.org/abs/1703.03373 (2017).
Other tune:
TuneControl
,
getNestedTuneResultsOptPathDf()
,
getNestedTuneResultsX()
,
getResamplingIndices()
,
getTuneResult()
,
makeModelMultiplexerParamSet()
,
makeModelMultiplexer()
,
makeTuneControlCMAES()
,
makeTuneControlDesign()
,
makeTuneControlGenSA()
,
makeTuneControlGrid()
,
makeTuneControlIrace()
,
makeTuneControlRandom()
,
makeTuneWrapper()
,
tuneParams()
,
tuneThreshold()
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