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 of, measures and/or fit:
“Those who, while they disapprove of the character and measures of a government, yield to it their allegiance and support are undoubtedly its most conscientious supporters, and so frequently the most serious obstacles to reform.”
—Henry David Thoreau (1817–1862)
“I hear America singing, the varied carols I hear,
Those of mechanics, each one singing his as it should be blithe and
strong,
The carpenter singing his as he measures his plank or beam,
The mason singing his as he makes ready for work, or leaves off
work,”
—Walt Whitman (1819–1892)
“Joshua fit de battle ob Jerico, Jerico, Jerico,
Joshua fit de battle ob Jerico,
An’ de walls come tumblin’ down.”
—Unknown. Joshua Fit De Battle of Jericho (l. 1–3)