Description Usage Arguments Value See Also
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 makeDiscreteParam
in the par.set
passed to tuneParams
.
Random search. All kinds of parameter types can be handled.
Evolutionary method 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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 |
[ |
log.fun |
[ |
final.dw.perc |
[ |
budget |
[ |
n.objectives |
[ |
impute.val |
[ |
learner |
[ |
mbo.control |
[ |
tune.threshold |
[ |
tune.threshold.args |
[ |
continue |
[ |
mbo.design |
[ |
... |
[any] |
maxit |
[ |
[TuneMultiCritControl
]. The specific subclass is one of
TuneMultiCritControlGrid
, TuneMultiCritControlRandom
,
TuneMultiCritControlNSGA2
, TuneMultiCritControlMBO
.
Other tune_multicrit: plotTuneMultiCritResultGGVIS
,
plotTuneMultiCritResult
,
tuneParamsMultiCrit
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