Hey all, I'm trying to use scikits-learn to fit a linear model using Ridge
regression. What I'd like to do is use cross validation to fit many
different models, and then look at the coefficients to see how stable they
are across different CV splits. (or perhaps to average them all together).
Right now I'm using cross_val_score because it makes parallelizing the fits
pretty easy (using an instance of KFold as the cross validation routine), I
get back a list of the scores for each CV split, but I don't get back the
fitted coefficient values that were calculated on each split.
Is there a way for me to access this information (the fitted coefficients
for each split)? It's clearly being calculated on each iteration, so I
assume there must be a way to report this back but I haven't been able to
figure it out...
Just to be clear - I'm not talking about the specified model parameters
(e.g., the alpha values), I'm talking about the fitted coefficients on each
CV split.
Thanks!
--
_____________________________________
PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/>
Blog Writer and Co-Director | Berkeley Science
Review<http://sciencereview.berkeley.edu/>
_____________________________________
------------------------------------------------------------------------------
Rapidly troubleshoot problems before they affect your business. Most IT
organizations don't have a clear picture of how application performance
affects their revenue. With AppDynamics, you get 100% visibility into your
Java,.NET, & PHP application. Start your 15-day FREE TRIAL of AppDynamics Pro!
http://pubads.g.doubleclick.net/gampad/clk?id=84349831&iu=/4140/ostg.clktrk
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
