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test_that("classif_logreg", {
# "did not converge":
m = suppressWarnings(glm(formula = binaryclass.formula, data = binaryclass.train, family = binomial))
p = predict(m, newdata = binaryclass.test, type = "response")
p.prob = 1 - p
p.class = as.factor(binaryclass.class.levs[ifelse(p > 0.5, 2, 1)])
suppressWarnings(testSimple("classif.logreg", binaryclass.df, binaryclass.target, binaryclass.train.inds, p.class))
suppressWarnings(testProb("classif.logreg", binaryclass.df, binaryclass.target, binaryclass.train.inds, p.prob))
tt = function(formula, data) {
glm(formula, data = data, family = binomial)
}
tp = function(model, newdata) {
p = predict(model, newdata, type = "response")
as.factor(binaryclass.class.levs[ifelse(p > 0.5, 2, 1)])
}
suppressWarnings(testCV("classif.logreg", binaryclass.df, binaryclass.target, tune.train = tt, tune.predict = tp))
})
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