if (requireNamespace("rpart")) {
task = mlr3::tsk("iris")
learner = mlr3::lrn("classif.rpart")
filter = flt("performance", learner = learner)
filter$calculate(task)
as.data.table(filter)
}
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("iris")
l = lrn("classif.rpart")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("performance", learner = l), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}
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