While Nadeau and Bengio (2003) consider K independent training and test splits we focus on the standard K-fold cross-validation procedure
The K-fold Cross Validation (KCV) technique is one of the most used approaches by practitioners for model selection and error es- timation of classifiers.
Abstract—In this paper we review the k–Fold Cross Valida- tion (KCV) technique
k-fold cross-validation. Variance analysis. Model selection. a b s t r a c t. Cross-validation (CV) is often used to estimate the generalization capability
variance of K-fold cross-validation. An analysis based on the eigende- composition of the covariance matrix of errors helps to better understand.
cross-validation los datos se dividen en K subconjuntos (folds). Uno de los subconjuntos se utiliza como datos de prueba y el resto (K-1) como datos de
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models allowing model selection and parameter
We describe various investigations for the assessment of performance of predictive regression models including different values of K in K-fold cross-validation
for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development.
In this work the serial and parallel implementation of leave-one-out and k-fold cross validation techniques is performed using the R software environment.