Coercion methods to BenchmarkAggr. For mlr3::BenchmarkResult this is a simple
wrapper around the BenchmarkAggr constructor called with mlr3::BenchmarkResult$aggregate().
Usage
as_benchmark_aggr(
obj,
task_id = "task_id",
learner_id = "learner_id",
independent = TRUE,
strip_prefix = TRUE,
...
)Arguments
- obj
(mlr3::BenchmarkResult|
matrix(1))
Passed to BenchmarkAggr$new().- task_id, learner_id, independent, strip_prefix
See BenchmarkAggr
$initialize().- ...
ANY
Passed to mlr3::BenchmarkResult$aggregate().
Examples
df = data.frame(tasks = factor(rep(c("A", "B"), each = 5),
levels = c("A", "B")),
learners = factor(paste0("L", 1:5)),
RMSE = runif(10), MAE = runif(10))
as_benchmark_aggr(df, task_id = "tasks", learner_id = "learners")
#> <BenchmarkAggr> of 10 rows with 2 tasks, 5 learners and 2 measures
#> tasks learners RMSE MAE
#> 1: A L1 0.1197089 0.9689532
#> 2: A L2 0.7020482 0.4389878
#> 3: A L3 0.1748738 0.5224602
#> 4: A L4 0.4117146 0.1327346
#> 5: A L5 0.6761042 0.5782789
#> 6: B L1 0.8840769 0.9950841
#> 7: B L2 0.8748552 0.9103015
#> 8: B L3 0.2907559 0.3498349
#> 9: B L4 0.3680931 0.8409293
#> 10: B L5 0.6419954 0.3926148
if (requireNamespaces(c("mlr3", "rpart"))) {
library(mlr3)
task = tsks(c("boston_housing", "mtcars"))
learns = lrns(c("regr.featureless", "regr.rpart"))
bm = benchmark(benchmark_grid(task, learns, rsmp("cv", folds = 2)))
# default measure
as_benchmark_aggr(bm)
# change measure
as_benchmark_aggr(bm, measures = msr("regr.rmse"))
}
#> <BenchmarkAggr> of 4 rows with 2 tasks, 2 learners and 1 measure
#> task_id learner_id rmse
#> 1: boston_housing featureless 9.183588
#> 2: boston_housing rpart 1.850790
#> 3: mtcars featureless 5.996558
#> 4: mtcars rpart 5.996558