Create an Initial Train/Validation/Test Split

initial_validation_split() creates a random three-way split of the data into a training set, a validation set, and a testing set. initial_validation_time_split() does the same, but instead of a random selection the training, validation, and testing set are in order of the full data set, with the first observations being put into the training set. group_initial_validation_split() creates similar random splits of the data based on some grouping variable, so that all data in a "group" are assigned to the same partition.

Usage

initial_validation_split(
  data,
  prop = c(0.6, 0.2),
  strata = NULL,
  breaks = 4,
  pool = 0.1,
  ...
)

initial_validation_time_split(data, prop = c(0.6, 0.2), ...)

group_initial_validation_split(
  data,
  group,
  prop = c(0.6, 0.2),
  ...,
  strata = NULL,
  pool = 0.1
)

# S3 method for class 'initial_validation_split'
training(x, ...)

# S3 method for class 'initial_validation_split'
testing(x, ...)

validation(x, ...)

# Default S3 method
validation(x, ...)

# S3 method for class 'initial_validation_split'
validation(x, ...)

Arguments

data

A data frame.

prop

A length-2 vector of proportions of data to be retained for training and validation data, respectively.

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.

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.

x

An object of class initial_validation_split.

Value

An initial_validation_split object that can be used with the training(), validation(), and testing() functions to extract the data in each split.

Details

training(), validation(), and testing() can be used to extract the resulting data sets. Use validation_set() to create an rset object for use with functions from the tune package such as tune::tune_grid().

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

validation_set()

Examples

set.seed(1353)
car_split <- initial_validation_split(mtcars)
train_data <- training(car_split)
validation_data <- validation(car_split)
test_data <- testing(car_split)

data(drinks, package = "modeldata")
drinks_split <- initial_validation_time_split(drinks)
train_data <- training(drinks_split)
validation_data <- validation(drinks_split)
c(max(train_data$date), min(validation_data$date))
#> [1] "2007-05-01" "2007-06-01"

data(ames, package = "modeldata")
set.seed(1353)
ames_split <- group_initial_validation_split(ames, group = Neighborhood)
train_data <- training(ames_split)
validation_data <- validation(ames_split)
test_data <- testing(ames_split)