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Disentangling Machine Learning Theory with Cross-Validation
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Does anyone see a link between machine learning's repeated epochs of training and the concept of cross-validation in linear modeling theory?
This article demonstrates what I perceive to be confusion about the validity of cross-validation combined with Bayesian optimization:
https://piotrekga.github.io/Pruned-Cross-Validation/
I am starting to believe cross-validation is actually a slower, less effective approximation of Bayesian inference. That opinion is informed by this Biometrika article from earlier this year (although I do not fully agree with the theoretical framework of coherence and prefer a more Jaynesian approach, but unfortunately he has been dead for more than 20 years):
https://academic.oup.com/biomet/article/107/2/489/5715611
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