Function to conduct nested resampling.

fselect_nested(
  method,
  task,
  learner,
  inner_resampling,
  outer_resampling,
  measure,
  term_evals = NULL,
  term_time = NULL,
  ...
)

Arguments

method

(character(1))
Key to retrieve fselector from mlr_fselectors dictionary.

task

(mlr3::Task)
Task to operate on.

learner

(mlr3::Learner).

inner_resampling

(mlr3::Resampling)
Resampling used for the inner loop.

outer_resampling

mlr3::Resampling)
Resampling used for the outer loop.

measure

(mlr3::Measure)
Measure to optimize.

term_evals

(integer(1))
Number of allowed evaluations.

term_time

(integer(1))
Maximum allowed time in seconds.

...

(named list())
Named arguments to be set as parameters of the fselector.

Value

mlr3::ResampleResult

Examples

rr = fselect_nested( method = "random_search", task = tsk("pima"), learner = lrn("classif.rpart"), inner_resampling = rsmp ("holdout"), outer_resampling = rsmp("cv", folds = 2), measure = msr("classif.ce"), term_evals = 4) # performance scores estimated on the outer resampling rr$score()
#> task task_id learner learner_id #> 1: <TaskClassif[47]> pima <AutoFSelector[40]> classif.rpart.fselector #> 2: <TaskClassif[47]> pima <AutoFSelector[40]> classif.rpart.fselector #> resampling resampling_id iteration prediction #> 1: <ResamplingCV[19]> cv 1 <PredictionClassif[19]> #> 2: <ResamplingCV[19]> cv 2 <PredictionClassif[19]> #> classif.ce #> 1: 0.2343750 #> 2: 0.2552083
# unbiased performance of the final model trained on the full data set rr$aggregate()
#> classif.ce #> 0.2447917