sage_aggregate_predictions: Aggregate Predictions by Coalition and Test Instance

In xplainfi: Feature Importance Methods for Global Explanations

Aggregate Predictions by Coalition and Test Instance

Description

Averages predictions across multiple samples (reference data or conditional samples) for each unique combination of coalition and test instance.

Usage

sage_aggregate_predictions(
  combined_data,
  predictions,
  task_type,
  class_names = NULL
)

Arguments

combined_data	(data.table) Data with columns .coalition_id, .test_instance_id, and feature columns.
predictions	(matrix or numeric) For classification: matrix of class probabilities. For regression: numeric vector of predictions.
task_type	(character(1)) Task type, either "classif" or "regr".
class_names	(character() or NULL: NULL) Character vector of class names. Required for classification, ignored for regression.

Value

data.table with columns:

• .coalition_id: Coalition identifier (integer)

• .test_instance_id: Test instance identifier (integer)

• For classification: One column per class with averaged probabilities (numeric)

• For regression: avg_pred column with averaged predictions (numeric)

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