Bias–Variance Tradeoff Among Cross-Validation Methods
Cross-validation methods differ in the bias and variance of their estimated test error. A validation-set approach trains on a relatively small subset of the data and may therefore overestimate test error. Leave-one-out cross-validation trains each model on observations, producing an approximately unbiased estimate, but its estimates tend to have higher variance. In -fold cross-validation, each model is trained on observations, so values such as or generally provide an intermediate bias with lower variance than leave-one-out cross-validation.
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