Package: CVST 0.2-3

Tammo Krueger

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.

Authors:Tammo Krueger, Mikio Braun

CVST_0.2-3.tar.gz
CVST_0.2-3.tar.gz(r-4.5-noble)CVST_0.2-3.tar.gz(r-4.4-noble)
CVST_0.2-3.tgz(r-4.4-emscripten)CVST_0.2-3.tgz(r-4.3-emscripten)
CVST.pdf |CVST.html✨
CVST/json (API)

Peer review:

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28 exports 2.42 score 3 dependencies 4 dependents 8 mentions 32 scripts 1.8k downloads

Last updated 3 years agofrom:1be4c26481. Checks:OK: 2. Indexed: yes.

Exports:blocksbumpscochranq.testconstructCVSTModelconstructDataconstructKlogRegLearnerconstructKRRLearnerconstructLearnerconstructParamsconstructSequentialTestconstructSVMLearnerconstructSVRLearnerCVdopplerfastCVgetCVSTTestgetNgetSubsetgetXheavisineisClassificationisRegressionnoisyDonohonoisySincnoisySineplotSequenceshuffleDatatestSequence

Dependencies:kernlablatticeMatrix