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#' Leave-One-Out Cross-Validation
#'
#' Leave-one-out (LOO) cross-validation uses one data point in the original
#' set as the assessment data and all other data points as the analysis set. A
#' LOO resampling set has as many resamples as rows in the original data set.
#' @inheritParams vfold_cv
#' @return An tibble with classes `loo_cv`, `rset`, `tbl_df`, `tbl`, and
#' `data.frame`. The results include a column for the data split objects and
#' one column called `id` that has a character string with the resample
#' identifier.
#' @examples
#' loo_cv(mtcars)
#' @export
loo_cv <- function(data, ...) {
check_dots_empty()
split_objs <- vfold_splits(data = data, v = nrow(data))
split_objs <-
list(
splits = map(split_objs$splits, change_class),
id = paste0("Resample", seq_along(split_objs$id))
)
## We remove the holdout indices since it will save space and we can
## derive them later when they are needed.
split_objs$splits <- map(split_objs$splits, rm_out)
new_rset(
splits = split_objs$splits,
ids = split_objs$id,
subclass = c("loo_cv", "rset")
)
}
change_class <- function(x) {
class(x) <- c("rsplit", "loo_split")
x
}
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