Inspect R Objects for Evaluation Risks

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_col = NULL,
  spatial_cols = NULL,
  ...
)

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_col

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

spatial_cols

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

...

Additional arguments passed to type-specific inspectors.

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

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

See also

borg_validate for complete workflow validation, borg for automated enforcement during evaluation.

Examples

# 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])