create.data.split.RdThis 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)object of class siamcat-class
integer number of cross-validation folds (needs to be
>=2), defaults to 2
integer, resampling rounds (values <= 1
deactivate resampling), defaults to 1
boolean, should the splits be stratified so that an equal
proportion of classes are present in each fold?, will be ignored for
regression tasks, defaults to TRUE
string, name of metadata variable to be inseparable,
defaults to NULL, see Details below
integer, control output: 0 for no output at all,
1 for only information about progress and success, 2 for
normal level of information and 3 for full debug information,
defaults to 1
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
If provided, the data split will take into account a metadata variable
for the data split (by providing the inseparable argument). For
example, if the data contains several samples for the same individual,
it makes sense to keep data from the same individual within the
same fold.
If inseparable is given, the stratify argument will be
ignored.
data(siamcat_example)
# simple working example
siamcat_split <- create.data.split(siamcat_example, num.folds=10,
num.resample=5, stratify=TRUE)
#> Features splitted for cross-validation successfully.