[Corpora-List] Leave-one out vs. 10-fold cross validation

[Georgios Paltoglou]

Hello to everyone,

I just wanted to ask whether anyone is aware of any formal reasons (e.g.
error distribution, decreased validity of results) for opting for 10-fold
cross validation instead of leave-one out, apart from the obvious reason
that it is more efficient and less time-consuming.

 

My 2 cents thought is that leave-one seems more realistic in the sense that
if the overall aim of a system is to provide the best classification for new
examples in an "application environment" given some training data, one would
naturally train it on the largest possible training subset.

 

Thank you for your responses.

 

Best regards,

George

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