Issue No. 03 - March (2010 vol. 32)
Juan Diego Rodríguez , University of the Basque Country, San Seabstian
Aritz Pérez , University of the Basque Country, San Sebastian-Donostia
Jose Antonio Lozano , University of the Basque Country, San Sebastian-Donostia
In the machine learning field, the performance of a classifier is usually measured in terms of prediction error. In most real-world problems, the error cannot be exactly calculated and it must be estimated. Therefore, it is important to choose an appropriate estimator of the error. This paper analyzes the statistical properties, bias and variance, of the k-fold cross-validation classification error estimator (k-cv). Our main contribution is a novel theoretical decomposition of the variance of the k-cv considering its sources of variance: sensitivity to changes in the training set and sensitivity to changes in the folds. The paper also compares the bias and variance of the estimator for different values of k. The experimental study has been performed in artificial domains because they allow the exact computation of the implied quantities and we can rigorously specify the conditions of experimentation. The experimentation has been performed for two classifiers (naive Bayes and nearest neighbor), different numbers of folds, sample sizes, and training sets coming from assorted probability distributions. We conclude by including some practical recommendation on the use of k-fold cross validation.
k-fold cross validation, prediction error, error estimation, bias and variance, decomposition of the variance, sources of sensitivity, supervised classification.
A. Pérez, J. A. Lozano and J. D. Rodríguez, "Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 569-575, 2009.