The Akaike Information Criterion and Artificial Intelligence
DOI:
https://doi.org/10.66721/fm7d2e97Keywords:
Akaike Information Criterion, Artificial Intelligence, LikelihoodAbstract
In this paper we use the Akaike information criterion to compare the use of parametric models with the use of very flexible models with a large parameter set, as in artificial intelligence models. It turns out that information is lost in models with many parameters, compared with using the real model.
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