Create a Validation Set

This function is deprecated because it's part of an approach to constructing a training, validation, and testing set by doing a sequence of two binary splits: testing / not-testing (with initial_split() or one of its variants) and then not-testing split into training/validation with validation_split(). Instead, now use initial_validation_split() or one if its variants to construct the three sets via one 3-way split.

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.

Note that the input data to validation_split(), validation_time_split(), and group_validation_split() should not contain the testing data. To create a three-way split directly of the entire data set, use initial_validation_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, ..., strata = NULL, pool = 0.1)

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.

...

These dots are for future extensions and must be empty.

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.

See also

initial_validation_split(), group_initial_validation_split(), validation_set()

Examples

cars_split <- initial_split(mtcars)
cars_not_testing <- training(cars_split)
validation_split(cars_not_testing, prop = .9)
#> Warning: `validation_split()` was deprecated in rsample 1.2.0.
#> ℹ Please use `initial_validation_split()` instead.
#> # Validation Set Split (0.9/0.1)  
#> # A tibble: 1 × 2
#>   splits         id        
#>   <list>         <chr>     
#> 1 <split [21/3]> validation
group_validation_split(cars_not_testing, cyl)
#> Warning: `group_validation_split()` was deprecated in rsample 1.2.0.
#> ℹ Please use `group_initial_validation_split()` instead.
#> # Group Validation Set Split (0.75/0.25)  
#> # A tibble: 1 × 2
#>   splits         id        
#>   <list>         <chr>     
#> 1 <split [15/9]> validation

data(drinks, package = "modeldata")
validation_time_split(drinks[1:200,])
#> Warning: `validation_time_split()` was deprecated in rsample 1.2.0.
#> ℹ Please use `initial_validation_time_split()` instead.
#> # Validation Set Split (0.75/0.25)  
#> # A tibble: 1 × 2
#>   splits           id        
#>   <list>           <chr>     
#> 1 <split [150/50]> validation

# Alternative
cars_split_3 <- initial_validation_split(mtcars)
validation_set(cars_split_3)
#> # A tibble: 1 × 2
#>   splits         id        
#>   <list>         <chr>     
#> 1 <split [19/6]> validation