On 10/23/2013 09:14 AM, Gael Varoquaux wrote:
>
> In doc/modules/cross_validation.rst:
>
> > @@ -231,6 +231,41 @@ not waste much data as only one sample is removed from the learning set::
> >    [0 1 2] [3]
> >
> >
> > +Potential users of LOO for model selection should weigh a few known caveats.
> > +When compared with *k*-fold cross validation, one builds *n* models from *n*
> > +samples instead of *k* models, where *n > k*. Moreover, each is trained on *n - 1*
> > +samples rather than *(k-1)n / k*. In both ways, assuming *k* is not too large
> > +and *k < n*, LOO is more computationally expensive than *k*-fold cross validation.
> > +Typically *k* should be between 5 and 10.
> > +Typically *k* should be between 5 and 10. I feel this "typically" 
> sentence doesn't really fit in here. Maybe rather say "n >> k" above?
> Well, it is true that folk culture in machine learning does recommend 
> n around 5 to 10.
I think the recommendation is good only I felt the sentence was out of 
place.