CV: Perform a k-fold Cross-validation

In CVST: Fast Cross-Validation via Sequential Testing

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 <[e-mail]>

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)

CVST documentation built on March 18, 2022, 5:59 p.m.