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)
“... moral certainty is certainty which is sufficient to regulate our behaviour, or which measures up to the certainty we have on matters relating to the conduct of life which we never normally doubt, though we know that it is possible, absolutely speaking, that they may be false.”
—René Descartes (1596–1650)
“Then comes my fit again. I had else been perfect,
Whole as the marble, founded as the rock,
As broad and general as the casing air.
But now I am cabined, cribbed, confined, bound in
To saucy doubts and fears.”
—William Shakespeare (1564–1616)