kfold_xval: k-fold cross-validation

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

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.

Usage

kfold_xval(folds = 10, method = "venetian", factor_name, ...)

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.
...	Additional slots and values passed to struct_class.

Value

A kfold_xval object.

Examples

D = iris_DatasetExperiment()
I = kfold_xval(factor_name='Species') *
    (mean_centre() + PLSDA(factor_name='Species'))
I = run(I,D,balanced_accuracy())

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.