This function prepares the cross-validation by splitting the
data into num.folds
training and test folds for
num.resample
times.
create.data.split <- (siamcat, num.folds = 2, num.resample = 1, stratify = TRUE,inseparable = NULL, verbose = 1)
siamcat | object of class siamcat-class |
---|---|
num.folds | number of cross-validation folds (needs to be |
num.resample | resampling rounds (values |
stratify | boolean, should the splits be stratified so that an equal
proportion of classes are present in each fold?, defaults to |
inseparable | column name of metadata variable, defaults to |
verbose | control output: |
object of class siamcat-class with the data_split
-slot
filled
This function splits the labels within a siamcat-class object and prepares the internal cross-validation for the model training (see train.model).
The function saves the training and test instances for the different
cross-validation folds within a list in the data_split
-slot of the
siamcat-class object, which is a list with four entries:
num.folds
the number of cross-validation folds
num.resample
the number of repetitions for the
cross-validation
training.folds
a list containing the indices for the
training instances
test.folds
a list containing the indices for the
test instances
data(siamcat_example) # simple working example siamcat_split <- create.data.split(siamcat_example, num.folds=10, num.resample=5, stratify=TRUE)#> Error in label$label: $ operator not defined for this S4 class## # example with a variable which is to be inseparable ## siamcat_split <- create.data.split(siamcat_example, num.folds=10, ## num.resample=5, stratify=FALSE, inseparable='Gender')