Description Usage Arguments Value See Also Examples
View source: R/tuneParamsMultiCrit.R
Optimizes the hyperparameters of a learner in a multi-criteria fashion.
Allows for different optimization methods, such as grid search, evolutionary strategies, etc.
You can select such an algorithm (and its settings)
by passing a corresponding control object. For a complete list of implemented algorithms look at
TuneMultiCritControl
.
1 2 | tuneParamsMultiCrit(learner, task, resampling, measures, par.set, control,
show.info = getMlrOption("show.info"), resample.fun = resample)
|
learner |
[ |
task |
[ |
resampling |
[ |
measures |
[list of |
par.set |
[ |
control |
[ |
show.info |
[ |
resample.fun |
[ |
[TuneMultiCritResult
].
Other tune_multicrit: TuneMultiCritControl
,
plotTuneMultiCritResultGGVIS
,
plotTuneMultiCritResult
1 2 3 4 5 6 7 8 9 10 11 | # multi-criteria optimization of (tpr, fpr) with NGSA-II
lrn = makeLearner("classif.ksvm")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
res = tuneParamsMultiCrit(lrn, sonar.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
plotTuneMultiCritResult(res, path = TRUE)
|
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