# NOT RUN {
set.seed(123)
tasks = mlr_tasks$mget(c("sonar", "spam"))
learners = mlr_learners$mget(c("classif.featureless", "classif.rpart"), predict_type = "prob")
resamplings = mlr_resamplings$get("cv3")
design = expand_grid(tasks = tasks, learners = learners, resamplings = resamplings)
print(design)
bmr = benchmark(design)
print(bmr)
bmr$tasks
bmr$learners
# first 5 individual resamplings
head(as.data.table(bmr, measures = c("classif.acc", "classif.auc")), 5)
# aggregate results
bmr$aggregate()
# aggregate results with hyperparameters as separate columns
mlr3misc::unnest(bmr$aggregate(params = TRUE), "params")
# extract resample result for classif.rpart
rr = bmr$aggregate()[learner_id == "classif.rpart", resample_result][[1]]
print(rr)
# access the confusion matrix of the first resampling iteration
rr$data$prediction[[1]]$confusion
# }
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