Simple Training/Test Set Splitting
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
first prop samples for training, instead of a random selection.
training and 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) testing(x)
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. This could be a single character value or a variable name that corresponds to a variable that exists in the data frame. |
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. |
... |
Not currently used. |
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. |
x |
An |
The strata argument causes the random sampling to be conducted
within the stratification variable. This can help ensure that the number
of data points in the training data is equivalent to the proportions in the
original data set. (Strata below 10% of the total are pooled together.)
An rsplit object that can be used with the training and testing
functions to extract the data in each split.
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 # 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
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