On Mon, Apr 30, 2012 at 6:38 PM, Olivier Grisel <[email protected]>wrote:
>
> Also another way to circumvent the n_samples change issue when doing
> CV-based model selection of sparse models might be to use the
> Bootstrap (sampling with replacement) and make the training size of
> the folds artificially fixed to a the total training set (by having
> redundant samples): I wonder if this is a good idea or not (having the
> same sample show up several times in the training set might be a bad
> idea).
>
>
Leave-one-out should work well but this restricts to small datasets or
estimators which can be efficiently computed...
Mathieu
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