Conditional Feature Importance

Implementation of CFI using modular sampling approach

References

Blesch, Kristin, Koenen, Niklas, Kapar, Jan, Golchian, Pegah, Burk, Lukas, Loecher, Markus, Wright, N. M (2025). “Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests.” Proceedings of the AAAI Conference on Artificial Intelligence, 39(15), 15596–15604. doi:10.1609/aaai.v39i15.33712 .

Super classes

xplainfi::FeatureImportanceMethod -> xplainfi::PerturbationImportance -> CFI

Methods

Public methods

• CFI$new()

• CFI$compute()

• CFI$clone()

Inherited methods

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

---

Method new()

Creates a new instance of the CFI class

Usage

CFI$new(
  task,
  learner,
  measure,
  resampling = NULL,
  features = NULL,
  groups = NULL,
  relation = "difference",
  iters_perm = 1L,
  sampler = NULL
)

Arguments

task, learner, measure, resampling, features, groups

Passed to PerturbationImportance.

relation

(character(1)) How to relate perturbed scores to originals. Can be overridden in $compute().

iters_perm

(integer(1)) Number of sampling iterations. Can be overridden in $compute().

sampler

(ConditionalSampler) Optional custom sampler. Defaults to instantiationg ARFSampler internally with default parameters.

---

Method compute()

Compute CFI scores

Usage

CFI$compute(iters_perm = NULL, store_backends = TRUE)

Arguments

iters_perm

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

store_backends

(logical(1)) Whether to store 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

CFI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
task = tgen("2dnormals")$generate(n = 100)
cfi = CFI$new(
  task = task,
  learner = lrn("classif.ranger", num.trees = 50, predict_type = "prob"),
  measure = msr("classif.ce")
)
#> ℹ No <ConditionalSampler> provided, using <ARFSampler> with default settings.
#> ℹ No <Resampling> provided
#> Using `resampling = rsmp("holdout")` with default `ratio = 0.67`.
cfi$compute()
cfi$importance()
#> Key: <feature>
#>    feature importance
#>     <char>      <num>
#> 1:      x1 0.00000000
#> 2:      x2 0.06060606