Data cleaning @ enrichment Could you link an example for a mixing?
Currently this is a bit if a mess with custom pickle persistence in a big for loop and custom transformers Thanks. Georg Joel Nothman <joel.noth...@gmail.com> schrieb am Mi. 16. Aug. 2017 um 13:51: > We certainly considered this over the many years that Pipeline caching has > been in the pipeline. Storing the fitted model means we can do both a > fit_transform and a transform on new data, and in many cases takes away the > pain point of CV over pipelines where downstream steps are varied. > > What transformer are you using where the transform is costly? Or is it > more a matter of you wanting to store the transformed data at each step? > > There are custom ways to do this sort of thing generically with a mixin if > you really want. > > On 16 August 2017 at 21:28, Georg Heiler <georg.kf.hei...@gmail.com> > wrote: > >> There is a new option in the pipeline: >> http://scikit-learn.org/stable/modules/pipeline.html#pipeline-cache >> How can I use this to also store the transformed data as I only want to >> compute the last step i.e. estimator during hyper parameter tuning and not >> the transform methods of the clean steps? >> >> Is there a possibility to apply this for crossvalidation? I would want to >> see all the folds precomputed and stored to disk in a folder. >> >> Regards, >> Georg >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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