If you use a cross validation scheme, where you estimate the residuals variance
on left-out data and compare it to the variance of the model with the intercept
only, then R^2 can be negative. This approach is an alternative to adjusted R^2
for model selection, and probably makes more sense when the model is penalized
(i.e. you're no longer doing maximum likelihood).
May be there's a more proper statistical term for that ?
HTH
Bertrand
----- Mail original -----
> De: "Ronnie Ghose" <ronnie.gh...@gmail.com>
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Jeudi 24 Janvier 2013 12:57:27
> Objet: Re: [Scikit-learn-general] (no subject)
> is it adjusted R^2? The usual R^2 can never be negative afaik
> http://en.wikipedia.org/wiki/Coefficient_of_determination
> On Wed, Jan 23, 2013 at 2:42 PM, Andreas Mueller <
> amuel...@ais.uni-bonn.de > wrote:
> > Am 23.01.2013 20:32, schrieb Ronnie Ghose:
>
> > > How can _best_score in GridSearchCV be negative? R^2 can only be
> > > from
>
> > > 0 to -1 ...?
>
> > R^2 can also be negative afaik. It is somewhat unstable for small
> > sample
>
> > sizes.
>
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