Create an Initial Train/Validation/Test SplitSource:
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.
testing() can be used to extract the
resulting data sets.
validation_set() to create an
rset object for use with functions from
the tune package such as
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 initial_validation_split training(x, ...) # S3 method for initial_validation_split testing(x, ...) validation(x, ...) # S3 method for default validation(x, ...) # S3 method for initial_validation_split validation(x, ...)
A data frame.
A length-2 vector of proportions of data to be retained for training and validation data, respectively.
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
strataare binned into quartiles.
A single number giving the number of bins desired to stratify a numeric stratification variable.
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.
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.
An object of class
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.
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)) #>  "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)