What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?

• Published in: Computational statistics Vol. 36; no. 3; pp. 2009 - 2031
• Main Authors: Marcot, Bruce G., Hanea, Anca M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Bayesian analysis; Bias; Calibration; Classification; Collinearity; Datasets; Economic Theory/Quantitative Economics/Mathematical Methods; Independent variables; Mathematics and Statistics; Network analysis; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Statistical methods; Statistics; Variables; Classification error; randomized subsets; sample size; Model validation

Cross-validation using randomized subsets of data—known as k-fold cross-validation—is a powerful means of testing the success rate of models used for classification. However, few if any studies have explored how values of k (number of subsets) affect validation results in models tested with data of...