`initial_split` creates a single binary split of the data into a training set and testing set. `training` and `testing` are used to extract the resulting data.

initial_split(data, prop = 3/4, strata = NULL, ...)

training(x)

testing(x)

Arguments

data

A data frame.

prop

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

strata

A variable that is used to conduct stratified sampling to create the resamples.

...

Not currently used.

x

An `rsplit` object produced by `initial_split`

Value

An `rset` object that can be used with the `training` and `testing` functions to extract the data in each split.

Details

The `strata` argument causes the random sampling to be conducted *within the stratification variable*. The can help ensure that the number of data points in the training data is equivalent to the proportions in the original data set.

Examples

set.seed(1353) car_split <- initial_split(mtcars) train_data <- training(car_split) test_data <- testing(car_split)