Fast Cross-Validation via Sequential Testing
The fast cross-validation via sequential testing (CVST) procedure 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 under-performing 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. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
| Package: | CVST |
| Type: | Package |
| Title: | Fast Cross-Validation via Sequential Testing |
| Version: | 0.2-2 |
| Date: | 2018-05-26 |
| Depends: | kernlab,Matrix |
| Author: | Tammo Krueger, Mikio Braun |
| Maintainer: | Tammo Krueger <tammokrueger@googlemail.com> |
| Description: | The fast cross-validation via sequential testing (CVST) procedure 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 under-performing 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. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts. |
| License: | GPL (>=2.0) |
Index of help topics:
CV Perform a k-fold Cross-validation
CVST-package Fast Cross-Validation via Sequential Testing
cochranq.test Cochran's Q Test with Permutation
constructCVSTModel Setup for a CVST Run.
constructData Construction and Handling of 'CVST.data'
Objects
constructLearner Construction of Specific Learners for CVST
constructParams Construct a Grid of Parameters
constructSequentialTest
Construct and Handle Sequential Tests.
fastCV The Fast Cross-Validation via Sequential
Testing (CVST) Procedure
noisyDonoho Generate Donoho's Toy Data Sets
noisySine Regression and Classification Toy Data SetTammo Krueger, Mikio Braun
Maintainer: Tammo Krueger <tammokrueger@googlemail.com>
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.
Abraham Wald. Sequential Analysis. Wiley, 1947.
W. G. Cochran. The comparison of percentages in matched samples. Biometrika, 37 (3-4):256–266, 1950.
M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32 (200):675–701, 1937.
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())
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