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Extract inner feature selection results of nested resampling. Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult. The function iterates over the AutoFSelector objects and binds the feature selection results to a data.table::data.table(). AutoFSelector must be initialized with store_fselect_instance = TRUE and resample() or benchmark() must be called with store_models = TRUE.



Data structure

The returned data table has the following columns:

  • experiment (integer(1))
    Index, giving the according row number in the original benchmark grid.

  • iteration (integer(1))
    Iteration of the outer resampling.

  • One column for each feature of the task.

  • One column for each performance measure.

  • features (character())
    Vector of selected feature set.

  • task_id (character(1)).

  • learner_id (character(1)).

  • resampling_id (character(1)).


at = auto_fselector(
  method = "random_search",
  learner = lrn("classif.rpart"),
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

resampling_outer = rsmp("cv", folds = 2)
rr = resample(tsk("iris"), at, resampling_outer, store_models = TRUE)

#>    iteration Petal.Length Petal.Width Sepal.Length Sepal.Width classif.ce
#> 1:         1         TRUE       FALSE        FALSE       FALSE       0.08
#> 2:         2        FALSE        TRUE        FALSE       FALSE       0.08
#>        features task_id              learner_id resampling_id
#> 1: Petal.Length    iris classif.rpart.fselector            cv
#> 2:  Petal.Width    iris classif.rpart.fselector            cv