Description Usage Arguments Value Author(s) References See Also Examples
CVST is an improved crossvalidation procedure which uses nonparametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full crossvalidation.
1 
train 
The data set as 
learner 
The learner as 
params 
the parameter grid as 
setup 
A 
test 
An independent test set that should be used at each step. If

verbose 
Should the procedure report the performance after each step? 
Returns the optimal parameter settings as determined by fast crossvalidation via sequential testing.
Tammo Krueger <tammokrueger@googlemail.com>
Tammo Krueger, Danny Panknin, and Mikio Braun. Fast crossvalidation via sequential testing. Journal of Machine Learning Research 16 (2015) 11031155. URL http://jmlr.org/papers/volume16/krueger15a/krueger15a.pdf.
CV
constructCVSTModel
constructData
constructLearner
constructParams
1 2 3 4  ns = noisySine(100)
svm = constructSVMLearner()
params = constructParams(kernel="rbfdot", sigma=10^(3:3), nu=c(0.05, 0.1, 0.2, 0.3))
opt = fastCV(ns, svm, params, constructCVSTModel())

Loading required package: kernlab
Loading required package: Matrix
Total number of params: 28
(sim: 26 alpha: 0.05 left: 28 )(sim: 14 alpha: 0.05 left: 28 )Skipped configurations: 0 (sim: 14 alpha: 0.05 left: 28 )Skipped configurations: 2 (sim: 12 alpha: 0.05 left: 26 )Skipped configurations: 2 (sim: 9 alpha: 0.05 left: 26 )Skipped configurations: 14 EARLY STOPPING!
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