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:
“One encounters very capable fathers abashed by their piano-playing daughters. Three measures of Schumann make them red with embarrassment.”
—Alfred Döblin (1878–1957)
“To the eyes of a god, mankind must appear as a species of bacteria which multiply and become progressively virulent whenever they find themselves in a congenial culture, and whose activity diminishes until they disappear completely as soon as proper measures are taken to sterilise them.”
—Aleister Crowley (1875–1947)
“Adolescents are travelers, far from home with no native land, neither children nor adults. They are jet-setters who fly from one country to another with amazing speed. Sometimes they are four years old, an hour later they are twenty-five. They don’t really fit anywhere. There’s a yearning for place, a search for solid ground.”
—Mary Pipher (20th century)