k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.
Arguments
- folds
(numeric, integer) The number of cross-validation folds. The default is
10.- method
(character) Fold selection method. Allowed values are limited to the following:
"venetian": Every nth sample is assigned to the same fold, where n is the number of folds."blocks": Blocks of adjacent samples are assigned to the same fold."random": Samples are randomly assigned to a fold.
The default is
"venetian".- factor_name
(character) The name of a sample-meta column to use.
- collect
(NULL, character) The name of a model output to collect over all bootstrap repetitions, in addition to the input metric. The default is
NULL.- ...
Additional slots and values passed to
struct_class.
Value
A kfold_xval object with the following output slots:
results | (data.frame) |
metric | (data.frame) |
metric.train | (numeric) |
metric.test | (numeric) |
collected | (list) |
Inheritance
A kfold_xval object inherits the following struct classes: [kfold_xval] >> [resampler] >> [iterator] >> [struct_class]
Examples
M = kfold_xval(
folds = 5,
method = "random",
factor_name = "V1",
collect = NULL)
D = iris_DatasetExperiment()
I = kfold_xval(factor_name='Species') *
(mean_centre() + PLSDA(factor_name='Species'))
I = run(I,D,balanced_accuracy())