The Akaike Information Criterion (AIC) is a statistical measure used to compare and select models, focusing on the trade-off between model complexity and goodness of fit. It provides a way to quantify how well a model explains the data while penalizing for the number of parameters used, helping to avoid overfitting. AIC is particularly useful in model selection as it allows for the evaluation of multiple models and aids in identifying the one that best balances simplicity and accuracy.
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