mlr (version 2.3)

tuneParamsMultiCrit: Hyperparameter tuning for multiple measures at once.

Description

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.

Usage

tuneParamsMultiCrit(learner, task, resampling, measures, par.set, control,
  show.info = getMlrOption("show.info"))

Value

[TuneMultiCritResult].

See Also

Other tune_multicrit: TuneMultiCritControl, TuneMultiCritControlGrid, TuneMultiCritControlNSGA2, TuneMultiCritControlRandom, makeTuneMultiCritControlGrid, makeTuneMultiCritControlNSGA2, makeTuneMultiCritControlRandom; plotTuneMultiCritResult

Examples

# 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)