I would expose it through a score function. In this way it can be called to evaluate 2 models (let's say model A with 4 params and model B with 10). Moreover, this could also be called by feature_selection.RFECV.
2016-01-01 19:19 GMT+01:00 Gael Varoquaux <gael.varoqu...@normalesup.org>: > It's there for a specific (sparse) model, where it makes sense: > > > http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html#sklearn.linear_model.LassoLarsIC > > How would you expose it in a more general way? > > Cheers, > > Gaël > > On Fri, Jan 01, 2016 at 05:31:20PM +0100, Marco De Nadai wrote: > > Hello everyone, > > I'm new to sklearn. Is there any reason behind the absence of Bayesian > > information criterion (BIC) and Akaike information criterion (AIC) in > sklearn > > (especially for model/feature selection). > > > Thanks :) > -- > Gael Varoquaux > Researcher, INRIA Parietal > NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France > Phone: ++ 33-1-69-08-79-68 > http://gael-varoquaux.info http://twitter.com/GaelVaroquaux > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > -- *Marco De Nadai* *Ph.D. student at Fondazione Bruno Kessler (FBK) - * *MobS Unit* *University of Trento* Via Sommarive, 18 - Povo 38123 Trento (TN) - Italy E-mail: dena...@fbk.eu LinkedIn: https://it.linkedin.com/in/marcodenadai
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