MarginalSAGE: Marginal SAGE

In xplainfi: Feature Importance Methods for Global Explanations

Marginal SAGE

Description

SAGE with marginal sampling (features are marginalized independently). This is the standard SAGE implementation.

Super classes

xplainfi::FeatureImportanceMethod -> xplainfi::SAGE -> MarginalSAGE

Methods

Public methods

• MarginalSAGE$new()

• MarginalSAGE$clone()

Inherited methods

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

---

Method new()

Creates a new instance of the MarginalSAGE class.

Usage

MarginalSAGE$new(
  task,
  learner,
  measure = NULL,
  resampling = NULL,
  features = NULL,
  n_permutations = 10L,
  batch_size = 5000L,
  n_samples = 100L,
  early_stopping = FALSE,
  se_threshold = 0.01,
  min_permutations = 10L,
  check_interval = 1L
)

Arguments

task, learner, measure, resampling, features, n_permutations, batch_size, n_samples, early_stopping, se_threshold, min_permutations, check_interval

Passed to SAGE.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MarginalSAGE$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

ConditionalSAGE

Examples

library(mlr3)
task = tgen("friedman1")$generate(n = 100)
sage = MarginalSAGE$new(
  task = task,
  learner = lrn("regr.ranger", num.trees = 50),
  measure = msr("regr.mse"),
  n_permutations = 3L,
  n_samples = 20
)
sage$compute()

xplainfi documentation built on Feb. 27, 2026, 1:08 a.m.