View source: R/getResamplingIndices.R
getResamplingIndices | R Documentation |
After you resampled a tuning or feature selection wrapper (see makeTuneWrapper)
with resample(..., extract = getTuneResult)
or resample(..., extract = getFeatSelResult)
this helper returns a list
with
the resampling indices used for the respective method.
getResamplingIndices(object, inner = FALSE)
object |
(ResampleResult) |
inner |
(logical) |
(list). One list for each outer resampling fold.
Other tune:
TuneControl
,
getNestedTuneResultsOptPathDf()
,
getNestedTuneResultsX()
,
getTuneResult()
,
makeModelMultiplexerParamSet()
,
makeModelMultiplexer()
,
makeTuneControlCMAES()
,
makeTuneControlDesign()
,
makeTuneControlGenSA()
,
makeTuneControlGrid()
,
makeTuneControlIrace()
,
makeTuneControlMBO()
,
makeTuneControlRandom()
,
makeTuneWrapper()
,
tuneParams()
,
tuneThreshold()
task = makeClassifTask(data = iris, target = "Species") lrn = makeLearner("classif.rpart") # stupid mini grid ps = makeParamSet( makeDiscreteParam("cp", values = c(0.05, 0.1)), makeDiscreteParam("minsplit", values = c(10, 20)) ) ctrl = makeTuneControlGrid() inner = makeResampleDesc("Holdout") outer = makeResampleDesc("CV", iters = 2) lrn = makeTuneWrapper(lrn, resampling = inner, par.set = ps, control = ctrl) # nested resampling for evaluation # we also extract tuned hyper pars in each iteration and by that the resampling indices r = resample(lrn, task, outer, extract = getTuneResult) # get tuning indices getResamplingIndices(r, inner = TRUE)
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