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Usage

fsi(
  task,
  learner,
  resampling,
  measures = NULL,
  terminator,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE
)

Arguments

task

(mlr3::Task)
Task to operate on.

learner

(mlr3::Learner)
Learner to optimize the feature subset for.

resampling

(mlr3::Resampling)
Resampling that is used to evaluated the performance of the feature subsets. Uninstantiated resamplings are instantiated during construction so that all feature subsets are evaluated on the same data splits. Already instantiated resamplings are kept unchanged.

measures

(mlr3::Measure or list of mlr3::Measure)
A single measure creates a FSelectInstanceSingleCrit and multiple measures a FSelectInstanceMultiCrit. If NULL, default measure is used.

terminator

(Terminator)
Stop criterion of the feature selection.

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?

Resources

Examples

# Feature selection on Palmer Penguins data set
task = tsk("penguins")
learner = lrn("classif.rpart")

# Construct feature selection instance
instance = fsi(
  task = task,
  learner = learner,
  resampling = rsmp("cv", folds = 3),
  measures = msr("classif.ce"),
  terminator = trm("evals", n_evals = 4)
)

# Choose optimization algorithm
fselector = fs("random_search", batch_size = 2)

# Run feature selection
fselector$optimize(instance)
#>    bill_depth bill_length body_mass flipper_length island  sex  year
#> 1:       TRUE        TRUE      TRUE           TRUE   TRUE TRUE FALSE
#>                                                      features classif.ce
#> 1: bill_depth,bill_length,body_mass,flipper_length,island,sex 0.06099669

# Subset task to optimal feature set
task$select(instance$result_feature_set)

# Train the learner with optimal feature set on the full data set
learner$train(task)

# Inspect all evaluated sets
as.data.table(instance$archive)
#>    bill_depth bill_length body_mass flipper_length island   sex  year
#> 1:      FALSE       FALSE     FALSE          FALSE   TRUE FALSE FALSE
#> 2:      FALSE        TRUE      TRUE          FALSE   TRUE FALSE FALSE
#> 3:       TRUE        TRUE      TRUE           TRUE   TRUE  TRUE FALSE
#> 4:       TRUE        TRUE     FALSE           TRUE   TRUE  TRUE FALSE
#>    classif.ce runtime_learners           timestamp batch_nr warnings errors
#> 1: 0.29064327            0.190 2022-11-17 04:11:48        1        0      0
#> 2: 0.06104755            0.185 2022-11-17 04:11:48        1        0      0
#> 3: 0.06099669            0.188 2022-11-17 04:11:49        2        0      0
#> 4: 0.06099669            0.191 2022-11-17 04:11:49        2        0      0
#>         resample_result
#> 1: <ResampleResult[21]>
#> 2: <ResampleResult[21]>
#> 3: <ResampleResult[21]>
#> 4: <ResampleResult[21]>