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test_that("regr_svm", {
requirePackagesOrSkip("e1071", default.method = "load")
parset.list = list(
list(),
list(kernel = "linear", epsilon = 0.2),
list(type = "nu-regression"),
list(type = "nu-regression", kernel = "radial", nu = 0.5)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(formula = regr.formula, data = regr.train)
pars = c(pars, parset)
m = do.call(e1071::svm, pars)
p = predict(m, newdata = regr.test)
old.predicts.list[[i]] = p
}
testSimpleParsets("regr.svm", regr.df, regr.target, regr.train.inds,
old.predicts.list, parset.list)
tt = e1071::svm
tp = function(model, newdata) predict(model, newdata)
testCVParsets("regr.svm", regr.df, regr.target, tune.train = tt,
tune.predict = tp, parset.list = parset.list)
})
test_that("classif_svm with many features", {
xt = cbind(as.data.frame(matrix(rnorm(4e4), ncol = 2e4)), x = 1:2)
xt.task = makeRegrTask("xt", xt, "x")
# the given task has many features, the formula interface fails
expect_silent(train("regr.svm", xt.task))
})
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