fselect_nested: Function for Nested Resampling

In mlr3fselect: Feature Selection for 'mlr3'

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 = NULL,
  ties_method = "least_features"
)

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	(bbotk::Terminator) Stop criterion of the feature selection.
store_fselect_instance	(logical(1)) If TRUE (default), stores the internally created FSelectInstanceBatchSingleCrit 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 CallbackBatchFSelect) List of callbacks.
ties_method	(character(1)) The method to break ties when selecting sets while optimizing and when selecting the best set. Can be "least_features" or "random". The option "least_features" (default) selects the feature set with the least features. If there are multiple best feature sets with the same number of features, one is selected randomly. The random method returns a random feature set from the best feature sets. Ignored if multiple measures are used.

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 Oct. 30, 2024, 9:19 a.m.