xai_prepare_sets: Prepare Training and Target Datasets from a caret Model

Description

Extracts and formats the training and/or target datasets from a machine learning model trained with caret::train, allowing for distinction between using the full training data or only the original subset used for modeling. It standardizes the class column to be named "class_efficiency" and positions it as the last column.

Usage

xai_prepare_sets(
  data,
  x,
  y,
  final_model,
  background,
  target,
  type,
  threshold,
  levels_order
)

Value

A list with two elements:

train_data: A data.frame representing the background dataset, with the class column renamed to "class_efficiency" and positioned last.
target_data: A data.frame representing the target dataset, formatted in the same way.

Arguments

data: A data.frame containing the original dataset used to train the model. Only needed when using "real" as background or target.
x: Not currently used. Reserved for future input variable selection.
y: Not currently used. Reserved for future output variable specification.
final_model: A trained model object of class "train" from the caret package.
background: A character string, either "train" or "real", specifying the background dataset used for explainability.
target: A character string, either "train" or "real", specifying the target dataset to be explained.
type: Not currently used. Reserved for future prediction types.
threshold: Not currently used. Reserved for future thresholding logic.
levels_order: A character vector specifying the levels of the response factor, typically c("not_efficient", "efficient"). Not currently used, but can help in reordering or relabeling.