Simple Training/Test Set SplittingSource:
initial_split creates a single binary split of the data into a training
set and testing set.
initial_time_split does the same, but takes the
prop samples for training, instead of a random selection.
group_initial_split creates splits of the data based
on some grouping variable, so that all data in a "group" is assigned to
the same split.
testing are used to extract the resulting data.
initial_split(data, prop = 3/4, strata = NULL, breaks = 4, pool = 0.1, ...) initial_time_split(data, prop = 3/4, lag = 0, ...) training(x, ...) # S3 method for default training(x, ...) # S3 method for rsplit training(x, ...) testing(x, ...) # S3 method for default testing(x, ...) # S3 method for rsplit testing(x, ...) group_initial_split(data, group, prop = 3/4, ..., strata = NULL, pool = 0.1)
A data frame.
The proportion of data to be retained for modeling/analysis.
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 value to include a lag between the assessment and analysis set. This is useful if lagged predictors will be used during training and testing.
rsplitobject produced by
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.
rsplit object that can be used with the
functions to extract the data in each split.
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_split(mtcars) train_data <- training(car_split) test_data <- testing(car_split) data(drinks, package = "modeldata") drinks_split <- initial_time_split(drinks) train_data <- training(drinks_split) test_data <- testing(drinks_split) c(max(train_data$date), min(test_data$date)) # no lag #>  "2011-03-01" "2011-04-01" # With 12 period lag drinks_lag_split <- initial_time_split(drinks, lag = 12) train_data <- training(drinks_lag_split) test_data <- testing(drinks_lag_split) c(max(train_data$date), min(test_data$date)) # 12 period lag #>  "2011-03-01" "2010-04-01" set.seed(1353) car_split <- group_initial_split(mtcars, cyl) train_data <- training(car_split) test_data <- testing(car_split)