| 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 |
learner |
The learner as |
params |
the parameter grid as |
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)