I'm looking to learn about how to conduct cross-validation, specifically the Holdout method (also called the 2-folds cross validation method). Does anyone have some texts or articles which they think would be helpful to read, or any other background information that might be helpful?
An inevitable resource for cross-validation in the context of Machine Learning is Kohavi's 1995 paper:
http://www.cs.iastate.edu/~jtian/cs573/Papers/Kohavi-IJCAI-95.pdf
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a recent "instant classic" on CV for model selection :
A survey of cross-validation procedures for model selection
Sylvain Arlot and Alain Celisse
Statist. Surv.
Volume 4 (2010), 1-274
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 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.
http://projecteuclid.org/euclid.ssu/1268143839
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