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Random k-fold. Ignores geography by construction.

Source: R/api.R
random_cv.Rd

Right when the rows really are independent, and the cleanest way to MEASURE what geography is worth: run it against spatial_cv() on the same points and the gap is the spatial optimism.

Usage

random_cv(
  k = 5L,
  test_frac = 0.15,
  group = "auto",
  seed = 42L,
  calibration_frac = 0
)

Arguments

k

Number of folds.

test_frac

Share held out as the test set, drawn first.

group

"auto" keeps the rows of one profile_id together when the table repeats profiles; NULL or "row" makes each row its own unit; a column name, or a vector with one label per row, names the groups.

seed

Seed for the partition only (see with_local_seed): a fixed plan reproduces even when the training seeds change.

calibration_frac

Share of ALL the points held out as a calibration set for the intervals, carved after the test set by the same criterion; 0 for none. It trains nothing and chooses nothing: dsm_final() predicts it and calibrates its "split" interval there.

Value

A resample_spec, as spatial_cv() returns.

Examples

random_cv(k = 10)
#> <resample_spec> random
#>   k               10
#>   test_frac       0.15
#>   group           auto
#>   seed            42
#>   calibration_frac 0