xplainfi (version 1.0.0)

PFI: Permutation Feature Importance

Description

Implementation of Permutation Feature Importance (PFI) using modular sampling approach. PFI measures the importance of a feature by calculating the increase in model error when the feature's values are randomly permuted, breaking the relationship between the feature and the target variable.

Super classes

xplainfi::FeatureImportanceMethod -> xplainfi::PerturbationImportance -> PFI

Methods

Public methods

• PFI$new()

• PFI$compute()

• PFI$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 PFI class

Usage

PFI$new(
  task,
  learner,
  measure = NULL,
  resampling = NULL,
  features = NULL,
  groups = NULL,
  relation = "difference",
  n_repeats = 1L,
  batch_size = NULL
)

Arguments

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

Passed to PerturbationImportance

---

Method compute()

Compute PFI scores

Usage

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

Arguments

n_repeats

(integer(1); NULL) 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

PFI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Permutation Feature Importance was originally introduced by Breiman (2001) as part of the Random Forest algorithm. The method works by:

1. Computing baseline model performance on the original dataset

2. For each feature, randomly permuting its values while keeping other features unchanged

3. Computing model performance on the permuted dataset

4. Calculating importance as the difference (or ratio) between permuted and original performance

References

Breiman L (2001). “Random Forests.” Machine Learning, 45(1), 5--32. tools:::Rd_expr_doi("10.1023/A:1010933404324"). Fisher A, Rudin C, Dominici F (2019). “All Models Are Wrong, but Many Are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.” Journal of Machine Learning Research, 20, 177. https://pmc.ncbi.nlm.nih.gov/articles/PMC8323609/. Strobl C, Boulesteix A, Kneib T, Augustin T, Zeileis A (2008). “Conditional Variable Importance for Random Forests.” BMC Bioinformatics, 9(1), 307. tools:::Rd_expr_doi("10.1186/1471-2105-9-307").