context("classif_rda")
test_that("classif_rda", {
requirePackagesOrSkip("klaR", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
m = klaR::rda(formula = multiclass.formula, data = multiclass.train)
p = predict(m, newdata = multiclass.test)$class
testSimple("classif.rda", multiclass.df, multiclass.target,
multiclass.train.inds, p)
parset.list = list(
list(),
list(gamma = 0.1, lambda = 0.1),
list(gamma = 0.5, lambda = 1),
list(gamma = 1, lambda = 0)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(formula = multiclass.formula, data = multiclass.train)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(klaR::rda, pars)
p = predict(m, newdata = multiclass.test)
old.predicts.list[[i]] = p$class
old.probs.list[[i]] = p$posterior
}
testSimpleParsets("classif.rda", multiclass.df, multiclass.target, multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.rda", multiclass.df, multiclass.target, multiclass.train.inds, old.probs.list, parset.list)
tt = klaR::rda
tp = function(model, newdata) predict(model, newdata)$class
testCVParsets("classif.rda", multiclass.df, multiclass.target, tune.train = tt, tune.predict = tp, parset.list = parset.list)
})
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