CVST (version 0.2-3)

CV: 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.

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

# NOT RUN {
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)
# }