test_that("classif_parallelForest", {
requirePackages("ParallelForest", default.method = "load")
parset.list = list(
list(),
list(numboots = 5L, numvars = 2L),
list(numboots = 10L, numsamps = 5L)
)
# parallelForest ist not reproducible with set.seed, so we just check for createability
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pf.classif.lrn = try(makeLearner("classif.parallelForest", par.vals = parset, predict.type = "response"))
expect_s3_class(pf.classif.lrn, "classif.parallelForest")
pf.classif.m = try(train(pf.classif.lrn, binaryclass.task))
# expect_s3_class(pf.classif.m, "WrappedModel")
pf.classif.p = try(predict(pf.classif.m, newdata = binaryclass.test))
expect_s3_class(pf.classif.p, c("PredictionClassif", "Prediction"))
}
})
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