Model selection criteria are statistical tools used to evaluate and compare different models to find the one that best fits a given dataset while avoiding overfitting or underfitting. These criteria help in determining which model is most effective at capturing the underlying patterns in data, considering aspects such as accuracy, complexity, and predictive power. The chosen model should balance fitting the data well and generalizing to new, unseen observations.
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