What is the Akaike Information Criterion (AIC)?

What is the Akaike Information Criterion (AIC)?

The Akaike Information Criterion (AIC) is a well-known common statistical criterion for model selection. The AIC is provided by the Japanese statistician. AIC finds a trade-off between the model’s simplicity and its goodness of fit. AIC principle states that the model complexity should be penalized to avoid overfitting which happens due to the noise in the data rather than the underlying pattern....

Why is AIC Important?

This criterion is important for the elimination of overfitting since it introduces a penalty equal to the number of model parameters used. It helps avoid situations when the chosen model happens to be more simple than required, which is also known as the underfitting issue as well as when the model is too complex, which is called overfitting....

Conclusion

It is crucial to learn how to calculate and interpret AIC in R for an efficient model selection and building of viable statistical models. This approach allows you to be hands-on in evaluating the various models through trial and error, guiding your analysis and bringing a deeper level of understanding of the data. AIC is an essential and powerful statistical machine in the data scientist’s or statisticians’ hands for model selection in their analyses....