context("classif_randomForestSRC")
test_that("classif_randomForestSRC", {
skip_on_os("mac")
requirePackagesOrSkip("randomForestSRC", default.method = "load")
parset.list = list(
list(ntree = 200),
list(ntree = 350, mtry = 5L),
list(ntree = 250, nodesize = 2, na.action = "na.impute",
importance = "permute", proximity = FALSE)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
parset = c(parset, list(data = binaryclass.train,
formula = binaryclass.formula, forest = TRUE))
set.seed(getOption("mlr.debug.seed"))
m = do.call(randomForestSRC::rfsrc, parset)
p = predict(m, newdata = binaryclass.test, membership = FALSE,
na.action = "na.impute")
old.predicts.list[[i]] = p$class
old.probs.list[[i]] = p$predicted[, 1]
}
testSimpleParsets("classif.randomForestSRC", binaryclass.df,
binaryclass.target, binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.randomForestSRC", binaryclass.df,
binaryclass.target, binaryclass.train.inds, old.probs.list, parset.list)
})
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