xplainfi (version 1.0.0)

CFI: Conditional Feature Importance

Description

Implementation of CFI using modular sampling approach

Super classes

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

Methods

Public methods

• CFI$new()

• CFI$compute()

• CFI$clone()

Inherited methods

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

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Method new()

Creates a new instance of the CFI class

Usage

CFI$new(
  task,
  learner,
  measure = NULL,
  resampling = NULL,
  features = NULL,
  groups = 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.

sampler

(ConditionalSampler) Optional custom sampler. Defaults to instantiating ConditionalARFSampler internally with default parameters.

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Method compute()

Compute CFI scores

Usage

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

Arguments

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.

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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.

References

Blesch K, Koenen N, Kapar J, Golchian P, Burk L, Loecher M, Wright M (2025). “Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests.” Proceedings of the AAAI Conference on Artificial Intelligence, 39(15), 15596--15604. tools:::Rd_expr_doi("10.1609/aaai.v39i15.33712").