Editing Cross-validation (statistics) (section)

==Measures of fit==

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 train the model.  It can be used to estimate any quantitative measure of fit that is appropriate for the data and model.  For example, for [[binary classification]] problems, each case in the validation set is either predicted correctly or incorrectly. In this situation the misclassification error rate can be used to summarize the fit, although other measures derived from information (e.g., counts, frequency) contained within a [[contingency table]] or [[confusion matrix]] could also be used.  When the value being predicted is continuously distributed, the [[mean squared error]], [[root mean squared error]] or [[median absolute deviation]] could be used to summarize the errors.