Marginal SAGE

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

See also

ConditionalSAGE

Super classes

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

Methods

Public methods

• MarginalSAGE$new()

• MarginalSAGE$clone()

Inherited methods

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

---

Method new()

Creates a new instance of the MarginalSAGE class.

Usage

MarginalSAGE$new(
  task,
  learner,
  measure,
  resampling = NULL,
  features = NULL,
  n_permutations = 10L,
  reference_data = NULL,
  batch_size = 5000L,
  max_reference_size = 100L,
  early_stopping = FALSE,
  convergence_threshold = 0.01,
  se_threshold = Inf,
  min_permutations = 10L,
  check_interval = 2L
)

Arguments

task, learner, measure, resampling, features, n_permutations, reference_data, batch_size, max_reference_size, early_stopping, convergence_threshold, 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.

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
)
#> ℹ No <Resampling> provided
#> Using `resampling = rsmp("holdout")` with default `ratio = 0.67`.
sage$compute()

# Use batching for memory efficiency with large datasets
sage$compute(batch_size = 1000)