Disentangling Machine Learning Theory with Cross-Validation

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:

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):

is a duplicate

This question has been asked before and has already been answered. It should be marked as a duplicate.

Please enter the URL of the proposed duplicate in the details field below.

not constructive

This question cannot be answered in a way that is helpful to anyone. It's not possible to learn something from possible answers, except for the solution for the specific problem of the asker.

Communities

Disentangling Machine Learning Theory with Cross-Validation

1 comment thread

0 answers