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#' Sampling for the Apparent Error Rate
#'
#' When building a model on a data set and re-predicting the same data, the
#' performance estimate from those predictions is often called the
#' "apparent" performance of the model. This estimate can be wildly
#' optimistic. "Apparent sampling" here means that the analysis and
#' assessment samples are the same. These resamples are sometimes used in
#' the analysis of bootstrap samples and should otherwise be
#' avoided like old sushi.
#'
#' @inheritParams vfold_cv
#' @return A tibble with a single row and classes `apparent`,
#' `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
#' apparent(mtcars)
#' @export
apparent <- function(data, ...) {
check_dots_empty()
splits <- rsplit(data, in_id = seq_len(nrow(data)), out_id = seq_len(nrow(data)))
# splits <- rm_out(splits)
class(splits) <- c("rsplit", "apparent_split")
split_objs <- tibble::tibble(splits = list(splits), id = "Apparent")
new_rset(
splits = split_objs$splits,
ids = split_objs$id,
attrib = NULL,
subclass = c("apparent", "rset")
)
}
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