borg_inspect: Inspect R Objects for Evaluation Risks

Description

borg_inspect() examines R objects for signals of information reuse that would invalidate model evaluation. It returns a structured assessment of detected risks.

Usage

borg_inspect(
  object,
  train_idx = NULL,
  test_idx = NULL,
  data = NULL,
  target = NULL,
  coords = NULL,
  ...
)

Value

A BorgRisk object containing:

risks: List of detected risk objects
n_hard: Count of hard violations
n_soft: Count of soft inflation warnings
is_valid: TRUE if no hard violations detected

Arguments

object

An R object to inspect. Supported types include:

Preprocessing: preProcess, recipe, prcomp
CV objects: trainControl, rsplit, vfold_cv
Model objects: train, lm, glm
Data frames with train/test split information

train_idx

Integer vector of training row indices. Required for data-level inspection.

test_idx

Integer vector of test row indices. Required for data-level inspection.

data

Optional data frame. Required when inspecting preprocessing objects to compare parameters against train-only statistics.

target

Optional name of the target/outcome column. If provided, checks for target leakage (features highly correlated with target).

coords

Optional character vector of coordinate column names. If provided, checks spatial separation between train and test.

...

Additional arguments passed to type-specific inspectors.

Details

borg_inspect() dispatches to type-specific inspectors based on the class of the input object. Each inspector looks for specific leakage patterns:

Preprocessing objects: Checks if parameters (mean, sd, loadings) were computed on data that includes test indices
CV objects: Validates that train/test indices do not overlap and that grouping structure is respected
Feature engineering: Checks if encodings, embeddings, or derived features used test data during computation

Examples

Run this code

# Inspect a preprocessing object
data(mtcars)
train_idx <- 1:25
test_idx <- 26:32

# BAD: preProcess fitted on full data (will detect leak)
pp_bad <- scale(mtcars[, -1])

# GOOD: preProcess fitted on train only
pp_good <- scale(mtcars[train_idx, -1])