Oh wow, thanks for the link, I just skimmed over the code, but this is an interesting idea snd looks like the sort of thing that would make my life easier in future. I will dig into that! That’s great, thanks!
> On Aug 19, 2015, at 12:58 AM, Stefan van der Walt <stef...@berkeley.edu> > wrote: > > On 2015-08-18 21:37:41, Sebastian Raschka <se.rasc...@gmail.com> > wrote: >> I think for “simple” linear models, it would be not a bad idea >> to save the weight coefficients in a log file or so. Here, I >> think that your model is really not that dependent on the >> changes in the scikit-learn code base (for example, imagine that >> you trained a model 10 years ago and published the results in a >> research paper, and today, someone asked you about this >> model). I mean, you know all about how a logistic regression, >> SVM etc. works, in the worst case you just use those weights to >> make the prediction on new data — I think in a typical “model >> persistence” case you don’t “update” your model anyways so >> “efficiency” would not be that big of a deal in a typical “worst >> case use case”. > > Agreed—this is exactly the type of use case I want to support. > Pickling won't work here, but using HDF5 like MNE does would > probably be close to ideal (thanks to Chris Holdgraf for the > heads-up): > > https://github.com/mne-tools/mne-python/blob/master/mne/_hdf5.py > > Stéfan > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
