Check Nested CV for Leakage

Validates a nested cross-validation setup for data leakage between outer and inner CV loops, and detects strategy mismatches where the inner CV uses random resampling despite data dependencies.

Usage

borg_check_nested_cv(
  inner_resamples,
  outer_train_idx,
  outer_test_idx,
  data,
  coords = NULL,
  time = NULL,
  groups = NULL
)

Arguments

inner_resamples

The inner resampling object. One of:

• A caret::train object

• A tune_results object from tidymodels

• A character string naming the inner CV method (e.g. "cv", "vfold_cv")

outer_train_idx

Integer vector. Indices of the outer training set.

outer_test_idx

Integer vector. Indices of the outer test set.

data

Data frame used for modeling.

coords

Character vector of coordinate column names (for spatial check).

time

Character. Time column name (for temporal check).

groups

Character. Group column name (for clustered check).

Value

A BorgRisk object with any detected risks.

Examples

# Check if inner random CV is appropriate given grouped data
d <- data.frame(
  site = rep(1:20, each = 10),
  x = rnorm(200),
  y = rep(rnorm(20), each = 10) + rnorm(200, sd = 0.5)
)
result <- borg_check_nested_cv(
  inner_resamples = "cv",
  outer_train_idx = 1:160,
  outer_test_idx = 161:200,
  data = d,
  groups = "site"
)