CVST: 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.
| Version: |
0.2-3 |
| Depends: |
kernlab, Matrix |
| Published: |
2022-02-21 |
| Author: |
Tammo Krueger, Mikio Braun |
| Maintainer: |
Tammo Krueger <tammokrueger at googlemail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
CVST results |
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