1
#' Leave-One-Out Cross-Validation
2
#'
3
#' Leave-one-out (LOO) cross-validation uses one data point in the original
4
#'  set as the assessment data and all other data points as the analysis set. A
5
#'  LOO resampling set has as many resamples as rows in the original data set.
6
#' @inheritParams vfold_cv
7
#' @return An tibble with classes `loo_cv`, `rset`, `tbl_df`, `tbl`, and
8
#'  `data.frame`. The results include a column for the data split objects and
9
#'  one column called `id` that has a character string with the resample
10
#'  identifier.
11
#' @examples
12
#' loo_cv(mtcars)
13
#' @export
14
loo_cv <- function(data, ...) {
15 1
  split_objs <- vfold_splits(data = data, v = nrow(data))
16 1
  split_objs <-
17 1
    list(splits = map(split_objs$splits, change_class),
18 1
         id = paste0("Resample", seq_along(split_objs$id)))
19

20
  ## We remove the holdout indices since it will save space and we can
21
  ## derive them later when they are needed.
22

23 1
  split_objs$splits <- map(split_objs$splits, rm_out)
24

25 1
  new_rset(splits = split_objs$splits,
26 1
           ids = split_objs$id,
27 1
           subclass = c("loo_cv", "rset"))
28
}
29

30
#' @export
31
print.loo_cv <- function(x, ...) {
32 1
  cat("#", pretty(x), "\n")
33 1
  class(x) <- class(x)[!(class(x) %in% c("loo_cv", "rset"))]
34 1
  print(x, ...)
35
}
36

37
change_class <- function(x) {
38 1
  class(x) <- c("rsplit", "loo_split")
39 1
  x
40
}
41

Read our documentation on viewing source code .

Loading