fastCV: The Fast Cross-Validation via Sequential Testing (CVST)...

In CVST: Fast Cross-Validation via Sequential Testing

The Fast Cross-Validation via Sequential Testing (CVST) Procedure

Description

CVST is an improved cross-validation procedure which uses non-parametric 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 cross-validation.

Usage

fastCV(train, learner, params, setup, test = NULL, verbose = TRUE)

Arguments

train	The data set as CVST.data object.
learner	The learner as CVST.learner object.
params	the parameter grid as CVST.params object.
setup	A CVST.setup object containing the necessary parameter for the CVST procedure.
test	An independent test set that should be used at each step. If NULL then the remaining data after learning a model at each step is used instead.
verbose	Should the procedure report the performance after each step?

Value

Returns the optimal parameter settings as determined by fast cross-validation via sequential testing.

Author(s)

Tammo Krueger <[e-mail]>

References

Tammo Krueger, Danny Panknin, and Mikio Braun. Fast cross-validation via sequential testing. Journal of Machine Learning Research 16 (2015) 1103-1155. URL https://jmlr.org/papers/volume16/krueger15a/krueger15a.pdf.

See Also

CV constructCVSTModel 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 = fastCV(ns, svm, params, constructCVSTModel())

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