TuneMultiCritControl | R Documentation |
The following tuners are available:
Grid search. All kinds of parameter types can be handled.
You can either use their correct param type and resolution
,
or discretize them yourself by always using ParamHelpers::makeDiscreteParam
in the par.set
passed to tuneParams.
Random search. All kinds of parameter types can be handled.
Evolutionary method mco::nsga2. Can handle numeric(vector) and integer(vector) hyperparameters, but no dependencies. For integers the internally proposed numeric values are automatically rounded.
Model-based/ Bayesian optimization. All kinds of parameter types can be handled.
makeTuneMultiCritControlGrid( same.resampling.instance = TRUE, resolution = 10L, log.fun = "default", final.dw.perc = NULL, budget = NULL ) makeTuneMultiCritControlMBO( n.objectives = mbo.control$n.objectives, 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 ) makeTuneMultiCritControlNSGA2( same.resampling.instance = TRUE, impute.val = NULL, log.fun = "default", final.dw.perc = NULL, budget = NULL, ... ) makeTuneMultiCritControlRandom( same.resampling.instance = TRUE, maxit = 100L, log.fun = "default", final.dw.perc = NULL, budget = NULL )
same.resampling.instance |
( |
resolution |
(integer) |
log.fun |
( |
final.dw.perc |
( |
budget |
( |
n.objectives |
( |
impute.val |
(numeric) |
learner |
(Learner | |
mbo.control |
(mlrMBO::MBOControl | |
tune.threshold |
( |
tune.threshold.args |
(list) |
continue |
( |
mbo.design |
(data.frame | |
... |
(any) |
maxit |
( |
(TuneMultiCritControl). The specific subclass is one of TuneMultiCritControlGrid, TuneMultiCritControlRandom, TuneMultiCritControlNSGA2, TuneMultiCritControlMBO.
Other tune_multicrit:
plotTuneMultiCritResult()
,
tuneParamsMultiCrit()
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