A survey of cross-validation procedures for model selection
Statistics Theory
2011-02-01 v1 Applications
Methodology
Machine Learning
Statistics Theory
Abstract
Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.
Cite
@article{arxiv.0907.4728,
title = {A survey of cross-validation procedures for model selection},
author = {Sylvain Arlot and Alain Celisse},
journal= {arXiv preprint arXiv:0907.4728},
year = {2011}
}