ML Anthology

No Free Lunch for Cross-Validation

Huaiyu Zhu, Richard Rohwer
NeCo 1996 pp. 1421-1426
doi:10.1162/NECO.1996.8.7.1421 /neco/1996/zhu1996neco-free/

Abstract

It is known theoretically that an algorithm cannot be good for an arbitrary prior. We show that in practical terms this also applies to the technique of “cross-validation,” which has been widely regarded as defying this general rule. Numerical examples are analyzed in detail. Their implications to researches on learning algorithms are discussed.

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Cite

Text

Zhu and Rohwer. "No Free Lunch for Cross-Validation." Neural Computation, 1996. doi:10.1162/NECO.1996.8.7.1421

Markdown

[Zhu and Rohwer. "No Free Lunch for Cross-Validation." Neural Computation, 1996.](https://mlanthology.org/neco/1996/zhu1996neco-free/) doi:10.1162/NECO.1996.8.7.1421

BibTeX

@article{zhu1996neco-free,
  title     = {{No Free Lunch for Cross-Validation}},
  author    = {Zhu, Huaiyu and Rohwer, Richard},
  journal   = {Neural Computation},
  year      = {1996},
  pages     = {1421-1426},
  doi       = {10.1162/NECO.1996.8.7.1421},
  volume    = {8},
  url       = {https://mlanthology.org/neco/1996/zhu1996neco-free/}
}