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Inspect R Objects for Evaluation Risks

Source: R/borg_inspect.R
borg_inspect.Rd

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,
  ...
)

Arguments

object

An R object to inspect. Supported types include:

  • Preprocessing: preProcess, recipe, prcomp, and base scale() output (a matrix carrying the "scaled:center"/"scaled:scale" attributes, or a data frame whose columns were replaced by scale() output)

  • 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.

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: standardisation fitted on full data (detected as a leak)
pp_bad <- scale(mtcars[, -1])
borg_inspect(pp_bad, train_idx = train_idx, test_idx = test_idx)

# GOOD: standardisation fitted on train only
pp_good <- scale(mtcars[train_idx, -1])
borg_inspect(pp_good, train_idx = train_idx, test_idx = test_idx)