test_that("paramtest classif.gausspr train", {
learner = lrn("classif.gausspr")
# The Learner actually calls the S4 method with class "formula", but this only creates the matrix
# and then calls the method for the class "matrix"
fun_list = list(
s4_helper(getMethod("gausspr", "matrix"))
)
exclude = c(
"x", # handled by mlr3
"y", # handled by mlr3
"class.weights", # handled by the task
"prob.model", # Would calculate the scale parameter of the Laplacian distribution fitted
"cross", # crossvalidation is done in mlr3
"subset", # mlr3
"type", # mlr3
"var", # only for regression
"variance.model", # only for regression
# on the residuals, can be implemented if wanted
# https://stackoverflow.com/questions/34323072/probability-model-in-kernlabksvm
# the kpar parameters are passed explicitly via "kpar"; "degree", "scale", "order", "offset"
"kpar",
"degree",
"sigma",
"scale",
"order",
"offset"
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "train")
expect_paramtest(paramtest)
})
test_that("paramtest classif.gausspr predict", {
learner = lrn("classif.gausspr")
# The Learner actually calls the S4 method with class "formula", but this only creates the matrix
# and then calls the method for the class "matrix"
fun_list = list(s4_helper(getMethod("predict", "gausspr")))
exclude = c(
# predict
"object",
"newdata",
"type"
)
paramtest = run_paramtest(learner, fun_list, exclude, tag = "predict")
expect_paramtest(paramtest)
})
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