tasks = list(tsk("penguins"), tsk("sonar"))
learners = list(lrn("classif.featureless"), lrn("classif.rpart"))
resamplings = list(rsmp("cv"), rsmp("subsampling"))
# Set a seed to ensure reproducibility of the resampling instantiation
set.seed(123)
grid = benchmark_grid(tasks, learners, resamplings)
# the resamplings are now instantiated
head(grid$resampling[[1]]$instance)
print(grid)
if (FALSE) {
benchmark(grid)
}
# paired
learner = lrn("classif.rpart")
task1 = tsk("penguins")
task2 = tsk("german_credit")
res1 = rsmp("holdout")
res2 = rsmp("holdout")
res1$instantiate(task1)
res2$instantiate(task2)
design = benchmark_grid(list(task1, task2), learner, list(res1, res2), paired = TRUE)
print(design)
# manual construction of the grid with data.table::CJ()
grid = data.table::CJ(task = tasks, learner = learners,
resampling = resamplings, sorted = FALSE)
# manual instantiation (not suited for a fair comparison of learners!)
Map(function(task, resampling) {
resampling$instantiate(task)
}, task = grid$task, resampling = grid$resampling)
if (FALSE) {
benchmark(grid)
}
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