Akaike Information Criterion |
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The Akaike Information Criterion (AIC) (Akaike, 1974) is a measure to help in the selection between candidate models. Using this criterion, the best model is the one with the lowest AIC. This criterion takes into account both the closeness of fit of the points to the model and the number of parameters used by the model.
It is calculated as:
where N is the number of data points, WSS is the weighted sum of squares of residuals and M is the number of model parameters.
See also Schwarz Criterion. |