Estimation of prediction error by using K-fold cross-validation

By Tadayoshi Fushiki. Statistics and computing, 2011

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A survey of Bayesian predictive methods for model assessment, selection and comparison (2012, positive): “The bias can be estimated and a corrected CV estimate can be computed (e.g., Burman, 1989; Fushiki, 2011).”
A Survey on Evaluation of Large Language Models (2023, methodology): “and obtain relatively more accurate model performance evaluation (Fushiki, 2011); Holdout validation divides the dataset into training and test sets, with a smaller calculation amount but potentially more significant bias; LOOCV is a unique K-fold cross-validation method where only one”
Veridical data science (2019, methodology): “CV works more effectively as a tool to select regularization parameters than as an estimate of prediction error, where it can incur high variability due to the often positive dependencies between the estimated prediction errors in the summation of the CV error (31).”