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cat("analyzeFeatSelResult")
test_that("analyzeFeatSelResult", {
lrn = makeLearner("classif.rpart")
rdesc = makeResampleDesc("Holdout")
ctrl.seq = makeFeatSelControlSequential(method = "sfs")
sf.seq = selectFeatures(learner = lrn, task = multiclass.task,
resampling = rdesc, control = ctrl.seq, show.info = FALSE)
expect_output(analyzeFeatSelResult(sf.seq, reduce = TRUE), "Petal.Width")
})
test_that("analyzeFeatSelResult with tune threshold (cf. issue #245)", {
ctrl = makeFeatSelControlSequential(method = "sfs", alpha = 0.1,
tune.threshold = TRUE)
rdesc = makeResampleDesc("Holdout")
lrn = makeLearner("classif.lda", predict.type = "prob")
task = subsetTask(sonar.task, features = paste("V", 11:16, sep = ""))
sfeats = selectFeatures(learner = lrn, task = task, resampling = rdesc,
control = ctrl, show.info = FALSE)
expect_output(analyzeFeatSelResult(sfeats, reduce = TRUE), "V11")
})
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