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.
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Tammo Krueger, Mikio Braun
Maintainer: Tammo Krueger <firstname.lastname@example.org>
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.
1 2 3 4
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!
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.