nested_cv: Nested or Double Resampling

In tidymodels/rsample: General Resampling Infrastructure

Nested or Double Resampling

Description

nested_cv() can be used to take the results of one resampling procedure and conduct further resamples within each split. Any type of resampling used in rsample can be used.

Usage

nested_cv(data, outside, inside)

Arguments

data	A data frame.
outside	The initial resampling specification. This can be an already created object or an expression of a new object (see the examples below). If the latter is used, the data argument does not need to be specified and, if it is given, will be ignored.
inside	An expression for the type of resampling to be conducted within the initial procedure.

Details

It is a bad idea to use bootstrapping as the outer resampling procedure (see the example below)

Value

An tibble with nested_cv class and any other classes that outer resampling process normally contains. The results include a column for the outer data split objects, one or more id columns, and a column of nested tibbles called inner_resamples with the additional resamples.

Examples

## Using expressions for the resampling procedures:
nested_cv(mtcars, outside = vfold_cv(v = 3), inside = bootstraps(times = 5))

## Using an existing object:
folds <- vfold_cv(mtcars)
nested_cv(mtcars, folds, inside = bootstraps(times = 5))

## The dangers of outer bootstraps:
set.seed(2222)
bad_idea <- nested_cv(mtcars,
  outside = bootstraps(times = 5),
  inside = vfold_cv(v = 3)
)

first_outer_split <- get_rsplit(bad_idea, 1)
outer_analysis <- analysis(first_outer_split)
sum(grepl("Camaro Z28", rownames(outer_analysis)))

## For the 3-fold CV used inside of each bootstrap, how are the replicated
## `Camaro Z28` data partitioned?
first_inner_split <- get_rsplit(bad_idea$inner_resamples[[1]], 1)
inner_analysis <- analysis(first_inner_split)
inner_assess <- assessment(first_inner_split)

sum(grepl("Camaro Z28", rownames(inner_analysis)))
sum(grepl("Camaro Z28", rownames(inner_assess)))

tidymodels/rsample documentation built on Sept. 29, 2024, 10:48 p.m.