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test_that("classif_binomial", {
parset.list1 = list(
list(family = binomial),
list(family = binomial(link = "logit")),
list(family = binomial(link = "cloglog"))
)
parset.list2 = list(
list(),
list(link = "logit"),
list(link = "cloglog")
)
old.predicts.list = list()
old.probs.list = list()
nof = 1:55 # remove feats
for (i in seq_along(parset.list1)) {
parset = parset.list1[[i]]
set.seed(getOption("mlr.debug.seed"))
m = glm(formula = binaryclass.formula, data = binaryclass.train[, -nof], family = parset$family)
p = predict(m, newdata = binaryclass.test[, -nof], type = "response")
p = 1 - p
p.class = as.factor(binaryclass.class.levs[ifelse(p > 0.5, 1, 2)])
old.predicts.list[[i]] = p.class
old.probs.list[[i]] = p
}
testSimpleParsets("classif.binomial", binaryclass.df[, -nof],
binaryclass.target, binaryclass.train.inds,
old.predicts.list, parset.list2)
testProbParsets("classif.binomial", binaryclass.df[, -nof],
binaryclass.target, binaryclass.train.inds,
old.probs.list, parset.list2)
})
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