Function to conduct nested resampling.

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

Arguments

method

(character(1))
Key to retrieve tuner from mlr_tuners 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.

search_space

(paradox::ParamSet).

...

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

Value

mlr3::ResampleResult

Examples

learner = lrn("classif.rpart") learner$param_set$values$minsplit = to_tune(1, 10) rr = tune_nested( method = "random_search", task = tsk("pima"), learner = learner, inner_resampling = rsmp ("holdout"), outer_resampling = rsmp("cv", folds = 2), measure = msr("classif.ce"), term_evals = 2, batch_size = 2) # check the inner results extract_inner_tuning_results(rr)
#> minsplit learner_param_vals x_domain classif.ce #> 1: 8 <list[2]> <list[1]> 0.234375 #> 2: 9 <list[2]> <list[1]> 0.250000
# aggregate performance of outer results rr$aggregate()
#> classif.ce #> 0.2682292