fselect_nested: Function for Nested Resampling
In mlr3fselect: Feature Selection for 'mlr3'

fselect_nested

R Documentation

Function for Nested Resampling

Description

Function to conduct nested resampling.

Usage

fselect_nested(
  fselector,
  task,
  learner,
  inner_resampling,
  outer_resampling,
  measure = NULL,
  term_evals = NULL,
  term_time = NULL,
  terminator = NULL,
  store_fselect_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = list()
)

Arguments

`fselector`	(FSelector) Optimization algorithm.
`task`	(mlr3::Task) Task to operate on.
`learner`	(mlr3::Learner) Learner to optimize the feature subset for.
`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. If `NULL`, default measure is used.
`term_evals`	(`integer(1)`) Number of allowed evaluations. Ignored if `terminator` is passed.
`term_time`	(`integer(1)`) Maximum allowed time in seconds. Ignored if `terminator` is passed.
`terminator`	(Terminator) Stop criterion of the feature selection.
`store_fselect_instance`	(`logical(1)`) If `TRUE` (default), stores the internally created FSelectInstanceSingleCrit with all intermediate results in slot `$fselect_instance`. Is set to `TRUE`, if `store_models = TRUE`
`store_benchmark_result`	(`logical(1)`) Store benchmark result in archive?
`store_models`	(`logical(1)`). Store models in benchmark result?
`check_values`	(`logical(1)`) Check the parameters before the evaluation and the results for validity?
`callbacks`	(list of CallbackFSelect) List of callbacks.

Value

mlr3::ResampleResult

Examples

# Nested resampling on Palmer Penguins data set
rr = fselect_nested(
  fselector = fs("random_search"),
  task = tsk("penguins"),
  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()

# Unbiased performance of the final model trained on the full data set
rr$aggregate()

mlr3fselect documentation built on March 7, 2023, 5:31 p.m.