Hi Michal, One way is to roll your own cross validation routine; it's not very complicated when specialised to a particular task.
I have also previously proposed that cross_val_score and Randomized/GridSearchCV provide an arbitrary callback parameter that could return the model or other diagnostic information. The right interface for this sort of thing is uncertain. Finally, you could consider my "remember" branch: https://github.com/jnothman/scikit-learn/tree/remember. It provides sklearn.memo.remember_model, which can wrap your base estimator, and will save a joblib dump of each model (in the directory specified by the memory parameter). However, to recover these models, the easiest way is to call fit() again on the remembered model, with the right portion of training data (and parameters if using grid search). [I am sorry this requires a patch/branch rather than a gist, but this functionality necessitates a polymorphic implementation of sklearn.base.clone.] Cheers, - Joel On 1 April 2014 06:23, Michal Romaniuk <[email protected]>wrote: > Hi, > > I am working on a problem where, in addition to the cross-validation > scores, I would like to be able to also record the full classifiers for > further analysis (visualisation etc.) Is there a way to do this? > > I tried to build a custom scoring function that returns a tuple of > different metrics (including the classifier itself) but it didn't work > as the scoring function seems to be required to return a number. > > Thanks, > Michal > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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