[Corpora-List] 10 fold cross validation vs. leave one out
emohamed at umail.iu.edu
Mon Apr 26 12:40:02 CEST 2010
> Message: 6
> Date: Mon, 26 Apr 2010 09:20:20 +0100
> From: "Georgios Paltoglou" <gpalto at gmail.com>
> Subject: [Corpora-List] Leave-one out vs. 10-fold cross validation
> To: <Corpora at uib.no>
> 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
> examples in an "application environment" given some training data, one
> naturally train it on the largest possible training subset.
> Thank you for your responses.
> Best regards,
I will not answer your question, but will provide a case where leave one out
did not work for me. When I worked with Arabic vowel restoration, the
classification had to be done at the letter level, but the evaluation had to
be reported at the word level. Also, there were several settings in which
the per letter accuracy did not correlate with the per word accuracy. In
some case, you just don't have the choice.
Emad Soliman Ali Mohamed
aka Emad Nawfal
Doctoral Candidate, Department of Linguistics,
Indiana University, Bloomington
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