R: Perform a k-fold Cross-validation

CV {CVST} | R Documentation

## Perform a k-fold Cross-validation

### Description

Performs the usual k-fold cross-validation procedure on a given data set, parameter grid and learner.

### Usage

    CV(data, learner, params, fold = 5, verbose = TRUE)

### Arguments

`data` | The data set as `CVST.data` object.
`learner` | The learner as `CVST.learner` object.
`params` | the parameter grid as `CVST.params` object.
`fold` | The number of folds that should be generated for each set of parameters.
`verbose` | Should the procedure report the performance for each model?

### Value

Returns the optimal parameter settings as determined by k-fold cross-validation.

### Author(s)

Tammo Krueger <tammokrueger@googlemail.com>

### References

M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, 36(2):111–147, 1974.

Sylvain Arlot, Alain Celisse, and Paul Painleve. A survey of cross-validation procedures for model selection. Statistics Surveys, 4:40–79, 2010.

### See Also

`fastCV` `constructData` `constructLearner` `constructParams`

### Examples

    ns = noisySine(100)
    svm = constructSVMLearner()
    params = constructParams(kernel="rbfdot", sigma=10^(-3:3), nu=c(0.05, 0.1, 0.2, 0.3))
    opt = CV(ns, svm, params)

[Package CVST version 0.2-2 Index]