# NOT RUN {
set.seed(123)
learners = list(
lrn("classif.featureless", predict_type = "prob"),
lrn("classif.rpart", predict_type = "prob")
)
design = benchmark_grid(
tasks = list(tsk("sonar"), tsk("spam")),
learners = learners,
resamplings = rsmp("cv", folds = 3)
)
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$aggregate()[learner_id == "classif.rpart", resample_result][[1]]
print(rr)
# access the confusion matrix of the first resampling iteration
rr$predictions()[[1]]$confusion
# }
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