validation_split() takes a single random sample (without replacement) of the original data set to be used for analysis. All other data points are added to the assessment set (to be used as the validation set). validation_time_split() does the same, but takes the first prop samples for training, instead of a random selection. group_validation_split() creates splits of the data based on some grouping variable, so that all data in a "group" is assigned to the same split.

## Usage

validation_split(data, prop = 3/4, strata = NULL, breaks = 4, pool = 0.1, ...)

validation_time_split(data, prop = 3/4, lag = 0, ...)

group_validation_split(data, group, prop = 3/4, ...)

## Arguments

data

A data frame.

prop

The proportion of data to be retained for modeling/analysis.

strata

A variable in data (single character or name) used to conduct stratified sampling. When not NULL, each resample is created within the stratification variable. Numeric strata are binned into quartiles.

breaks

A single number giving the number of bins desired to stratify a numeric stratification variable.

pool

A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.

...

Not currently used.

lag

A value to include a lag between the assessment and analysis set. This is useful if lagged predictors will be used during training and testing.

group

A variable in data (single character or name) used for grouping observations with the same value to either the analysis or assessment set within a fold.

## Value

An tibble with classes validation_split, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and a column called id that has a character string with the resample identifier.

## Details

With a strata argument, the random sampling is conducted within the stratification variable. This can help ensure that the resamples have equivalent proportions as the original data set. For a categorical variable, sampling is conducted separately within each class. For a numeric stratification variable, strata is binned into quartiles, which are then used to stratify. Strata below 10% of the total are pooled together; see make_strata() for more details.

## Examples

validation_split(mtcars, prop = .9)
#> # Validation Set Split (0.9/0.1)
#> # A tibble: 1 × 2
#>   splits         id
#>   <list>         <chr>
#> 1 <split [28/4]> validation

data(drinks, package = "modeldata")
validation_time_split(drinks)
#> # Validation Set Split (0.75/0.25)
#> # A tibble: 1 × 2
#>   splits           id
#>   <list>           <chr>
#> 1 <split [231/78]> validation

group_validation_split(mtcars, cyl)
#> # Group Validation Set Split (0.75/0.25)
#> # A tibble: 1 × 2
#>   splits          id
#>   <list>          <chr>
#> 1 <split [21/11]> validation