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(Neural Computation. 1999;11:863-870.)
© 1999 The MIT Press

Note

On Cross Validation for Model Selection

Isabelle Rivals

Laboratoire d'Electronique, ESPCI, 75231 Paris Cedex 05, France

Léon Personnaz

Laboratoire d'Electronique, ESPCI, 75231 Paris Cedex 05, France

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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