View source: R/tuneParamsMultiCrit.R
tuneParamsMultiCrit | R Documentation |
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.
tuneParamsMultiCrit( learner, task, resampling, measures, par.set, control, show.info = getMlrOption("show.info"), resample.fun = resample )
learner |
(Learner | |
task |
(Task) |
resampling |
(ResampleInstance | ResampleDesc) |
measures |
[list of Measure) |
par.set |
(ParamHelpers::ParamSet) |
control |
(TuneMultiCritControl) |
show.info |
( |
resample.fun |
(closure) |
(TuneMultiCritResult).
Other tune_multicrit:
TuneMultiCritControl
,
plotTuneMultiCritResult()
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.