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

fastCV {CVST} | R Documentation

## 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 <tammokrueger@googlemail.com>

### References

Tammo Krueger, Danny Panknin, and Mikio Braun. Fast cross-validation via sequential testing. Journal of Machine Learning Research 16 (2015) 1103-1155. URL http://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())

[Package CVST version 0.2-2 Index]