Overfitting

Overfitting (Statistical Model Validation) (GWU EMSE-271)
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[Need to find the statement about the tendency to overfit and the problems associated I remember from somewhere.

As "the fit of the model improves," "its ability to fit observations outside the sample becomes worse because the patterns captured in the sample do not generalize to the population." - Lattin

"In statistics, **overfitting** occurs when a statistical model describes random error or noise instead of the underlying relationship.

"Overfitting generally occurs when a model is excessively complex, such as having too many degrees of freedom, in relation to the amount of data available. A model which has been overfit will generally have poor predictive performance, as it can exaggerate minor fluctuations in the data.

"The potential for overfitting depends not only on the number of parameters and data but also the conformability of the model structure with the data shape, and the magnitude of model error compared to the expected level of noise or error in the data.- [|Wikipedia]


 * Sources:**
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), page 71
 * Overfitting. (2009, August 4). In //Wikipedia, The Free Encyclopedia//. Retrieved 14:48, December 8, 2009, from []
 * EMSE 271, Fall 2009