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 like positive predictive value 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.
Read more about this topic: Cross-validation (statistics)
Famous quotes containing the words measures and/or fit:
“the dread
That how we live measures our own nature,
And at his age having no more to show
Than one hired box should make him pretty sure
He warranted no better,”
—Philip Larkin (1922–1985)
“Love stories are only fit for the solace of people in the insanity of puberty. No healthy adult human being can really care whether so-and-so does or does not succeed in satisfying his physiological uneasiness by the aid of some particular person or not.”
—Aleister Crowley (1875–1947)