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context("getOOBPreds")
test_that("getOOBPreds", {
lrns = list(
makeLearner("classif.randomForest"),
makeFilterWrapper(learner = "classif.randomForest",
fw.method = "FSelectorRcpp_information.gain",
fw.abs = 2))
task = subsetTask(binaryclass.task, subset = c(10:20, 180:190),
features = getTaskFeatureNames(binaryclass.task)[12:15])
for (lrn in lrns) {
mod = train(lrn, task)
oob = getOOBPreds(mod, task)
pred = predict(mod, task)
expect_true(is.numeric(performance(oob, measures = list(acc))))
expect_equal(dim(oob$data), dim(pred$data))
expect_equal(names(oob$data), names(pred$data))
expect_equal(names(oob), names(pred))
}
})
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