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

xplainfi::FeatureImportanceMethod -> xplainfi::PerturbationImportance -> RFI

Methods

Public methods

• RFI$new()

• RFI$compute()

• RFI$clone()

Inherited methods

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

---

Method new()

Creates a new instance of the RFI class

Usage

RFI$new(
  task,
  learner,
  measure,
  resampling = NULL,
  features = NULL,
  groups = NULL,
  conditioning_set = NULL,
  relation = "difference",
  n_repeats = 1L,
  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.

---

Method 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.

---

Method 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.ranger", num.trees = 50),
  measure = msr("regr.mse"),
  conditioning_set = c("important1")
)
#> ℹ No <ConditionalSampler> provided, using <ConditionalARFSampler> with default settings.
#> ℹ No <Resampling> provided
#> Using `resampling = rsmp("holdout")` with default `ratio = 0.67`.
rfi$compute()
rfi$importance()
#> Key: <feature>
#>          feature  importance
#>           <char>       <num>
#>  1:   important1  0.00000000
#>  2:   important2  4.57328636
#>  3:   important3  0.84236459
#>  4:   important4  9.35752255
#>  5:   important5  1.12430811
#>  6: unimportant1 -0.29889537
#>  7: unimportant2 -0.10636319
#>  8: unimportant3 -0.02970777
#>  9: unimportant4  0.07256787
#> 10: unimportant5 -0.40156250