Relative Feature Importance

RFI generalizes CFI and PFI with arbitrary conditioning sets and samplers.

References

König G, Molnar C, Bischl B, Grosse-Wentrup M (2021). “Relative Feature Importance.” In 2020 25th International Conference on Pattern Recognition (ICPR), 9318–9325. doi:10.1109/ICPR48806.2021.9413090 .

Super classes

FeatureImportanceMethod -> PerturbationImportance -> RFI

Methods

Public methods

• RFI$new()

• RFI$compute()

• RFI$clone()

Inherited methods

• FeatureImportanceMethod$obs_loss()
• FeatureImportanceMethod$print()
• FeatureImportanceMethod$reset()
• FeatureImportanceMethod$scores()
• PerturbationImportance$importance()

---

RFI$new()

Creates a new instance of the RFI class

Usage

RFI$new(
  task,
  learner,
  measure = NULL,
  resampling = NULL,
  features = NULL,
  groups = NULL,
  conditioning_set = NULL,
  relation = "difference",
  n_repeats = 30L,
  batch_size = NULL,
  sampler = NULL
)

Arguments

task, learner, measure, resampling, features, groups, relation, n_repeats, batch_size

Passed to PerturbationImportance.

conditioning_set

(character()) Set of features to condition on. Can be overridden in $compute(). Default (character(0)) is equivalent to PFI. In CFI, this would be set to all features except that of interest.

sampler

(ConditionalSampler) Optional custom sampler. Defaults to ConditionalARFSampler.

---

RFI$compute()

Compute RFI scores

Usage

RFI$compute(
  conditioning_set = NULL,
  n_repeats = NULL,
  batch_size = NULL,
  store_models = TRUE,
  store_backends = TRUE
)

Arguments

conditioning_set

(character()) Set of features to condition on. If NULL, uses the stored parameter value.

n_repeats

(integer(1)) Number of permutation iterations. If NULL, uses stored value.

batch_size

(integer(1) | NULL: NULL) Maximum number of rows to predict at once. If NULL, uses stored value.

store_models, store_backends

(logical(1): TRUE) Whether to store fitted models / data backends, passed to mlr3::resample internally for the initial fit of the learner. This may be required for certain measures and is recommended to leave enabled unless really necessary.

---

RFI$clone()

The objects of this class are cloneable with this method.

Usage

RFI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
task = tgen("friedman1")$generate(n = 200)
rfi = RFI$new(
  task = task,
  learner = lrn("regr.rpart"),
  measure = msr("regr.mse"),
  conditioning_set = c("important1"),
  sampler = ConditionalGaussianSampler$new(task),
  n_repeats = 5
)
#> ℹ No <Resampling> provided, using `resampling = rsmp("holdout", ratio = 2/3)`
#>   (test set size: 67)
rfi$compute()
rfi$importance()
#> Key: <feature>
#>          feature importance
#>           <char>      <num>
#>  1:   important1   0.000000
#>  2:   important2   4.131735
#>  3:   important3   0.000000
#>  4:   important4  14.631406
#>  5:   important5   0.000000
#>  6: unimportant1   1.614474
#>  7: unimportant2   0.000000
#>  8: unimportant3   0.000000
#>  9: unimportant4   0.000000
#> 10: unimportant5   0.000000