Category:Cross-Validation

This category contains results about Cross-Validation.
Definitions specific to this category can be found in Definitions/Cross-Validation.

Cross-validation is a technique in statistics in which:

$(1): \quad$ data are randomly partitioned into $2$ or more subsets

$(2): \quad$ A model, for example a regression model, is fitted to all but one of these subsets

$(3): \quad$ A prediction error of the fitted model when applied to the omitted set is calculated.

Each subset is omitted in turn, and a combined estimated prediction error is obtained.

This method is useful for testing the overall goodness of fit of a model and for detecting outliers.