Statistical Prediction
AIC, or Akaike Information Criterion, is a measure used to compare different statistical models, helping to identify the model that best explains the data with the least complexity. It balances goodness of fit with model simplicity by penalizing for the number of parameters in the model, promoting a balance between overfitting and underfitting. This makes AIC a valuable tool for model selection across various contexts.
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