kfold_xval {structToolbox} | R Documentation

## k-fold cross-validation

### 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 with the following `output` slots:

`results` | (data.frame)
`metric` | (data.frame)
`metric.train` | (numeric)
`metric.test` | (numeric)

### Examples

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

[Package structToolbox version 1.6.1 Index]