Society of Actuaries (SOA) PA Practice Exam 2026 - Free Actuarial Practice Questions and Study Guide

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What does AIC stand for in model selection?

Akaike Information Criterion

AIC stands for Akaike Information Criterion, which is a widely used metric in model selection. It provides a means of comparing different statistical models to determine which one best fits a given dataset. The criterion is based on the likelihood of the model and includes a penalty for the number of parameters used, which helps to prevent overfitting.

The lower the AIC value, the better the model is considered to be, as it suggests a balance between goodness of fit and model complexity. This is crucial because a model that is too complex may fit the training data well but perform poorly on new, unseen data. The AIC is particularly useful because it allows for the selection of models even when the full distribution of the data is not known, making it versatile across different applications in statistical modeling.

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Adjusted Information Criterion

Absolute Information Count

Averaged Information Criteria

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