On 03/10/2013 12:01 PM, Joel Nothman wrote: > Firstly thank you to the devs for a great toolkit. > > I am using sklearn's GridSearchCV for a classification with the F1 > metric. GridSearchCV.fit() produces a .cv_scores_ attribute which > allows me to view the scores for each fold for each point in the grid. > But it does not let me view the precision and recall for each fold, or > even overall for a grid point, from which F1 is calculated. > Is there a particular reason why you don't use the f1 score for doing the grid search? If this is your ultimate goal, I don't see a reason to use accuracy. In the current stable, you can use ``score_func=f1_score``, in the current developer version you can use ``scoring='f1'``.
> I can see two ways to make this data available: > * allow the user to provide an arbitrary diagnostics function which is > run on each fold's predictions and whose output is stored with > cv_scores_ (or one could even store the learnt parameters for each fold) This should be possible in the current developer version with the introduction of the ``scoring`` parameter. We deemed storing all models infeasible because of the large memory requirement of some models. We could optionally reintroduce it. Hth, Andy ------------------------------------------------------------------------------ Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the endpoint security space. For insight on selecting the right partner to tackle endpoint security challenges, access the full report. http://p.sf.net/sfu/symantec-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
