ML Anthology

On Cross Validation for Model Selection

Isabelle Rivals, Léon Personnaz
NeCo 1999 pp. 863-870
doi:10.1162/089976699300016476 /neco/1999/rivals1999neco-cross/

Abstract

In response to Zhu and Rower (1996), a recent communication (Goutte, 1997) established that leave-one-out cross validation is not subject to the “no-free-lunch” criticism. Despite this optimistic conclusion, we show here that cross validation has very poor performances for the selection of linear models as compared to classic statistical tests. We conclude that the statistical tests are preferable to cross validation for linear as well as for nonlinear model selection.

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Cite

Text

Rivals and Personnaz. "On Cross Validation for Model Selection." Neural Computation, 1999. doi:10.1162/089976699300016476

Markdown

[Rivals and Personnaz. "On Cross Validation for Model Selection." Neural Computation, 1999.](https://mlanthology.org/neco/1999/rivals1999neco-cross/) doi:10.1162/089976699300016476

BibTeX

@article{rivals1999neco-cross,
  title     = {{On Cross Validation for Model Selection}},
  author    = {Rivals, Isabelle and Personnaz, Léon},
  journal   = {Neural Computation},
  year      = {1999},
  pages     = {863-870},
  doi       = {10.1162/089976699300016476},
  volume    = {11},
  url       = {https://mlanthology.org/neco/1999/rivals1999neco-cross/}
}