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Akaike Data Criterion (AICc) The Akaike Data Criterion (AIC) gives a way for measuring the relative performance in fitting a regression design to some given established of data. Established over the notion of data entropy, the criterion offers a relative measure of the knowledge misplaced in employing a design to explain the info. Far more particularly, it provides a tradeoff in between maximizing the probability for that believed design (the same as reducing the residual sum of squares if the data is normally distributed) and preserving the number of free of charge parameters within the model to your minimum amount, lessening its complexity. Even though goodness-of-healthy is almost always enhanced by introducing much more parameters, overfitting will increase the sensitivity on the product to changes within the input details and will spoil its predictive capacity.
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