Assume a model with one or more unknown parameters, and a data set to which the model can be fit (the training data set). The fitting process optimizes the model parameters to … See more

When cross-validation is used simultaneously for selection of the best set of hyperparameters and for error estimation (and assessment of generalization capacity), a nested cross-validation is required. Many variants exist. At least two variants … See more

When users apply cross-validation to select a good configuration $${\displaystyle \lambda }$$, then they might want to balance the cross-validated choice with their own estimate of the configuration. In this way, they can attempt to counter the … See more

Most forms of cross-validation are straightforward to implement as long as an implementation of the prediction method being studied is available. In particular, the prediction method can be a "black box" – there is no need to have access to the internals of its … See more

Two types of cross-validation can be distinguished: exhaustive and non-exhaustive cross-validation.
Exhaustive cross-validation
Exhaustive cross-validation methods are cross-validation methods which learn and test on all possible … See more

The goal of cross-validation is to estimate the expected level of fit of a model to a data set that is independent of the data that were used to … See more

Suppose we choose a measure of fit F, and use cross-validation to produce an estimate F of the expected fit EF of a model to an independent data set drawn from the same … See more