@Gilles, Thanks for the link. Those classes basically implement a paper that has a specific idea of RandomForests™ (no kidding, it's trademarked), with bootstrapping, oob estimation, and n trees trained on the same data.
I'm basically looking to take pre-trained classifiers and allows you to combine the predicted probabilities in custom ways, like favoring some classifiers over others, etc. Not that RandomForests™ are not useful--they could be the building block classifiers in such a system. @Oliver's writeup would exactly solve my problem. Cheers, Doug On Wed, Sep 26, 2012 at 2:30 AM, Gilles Louppe <[email protected]> wrote: > @Doug: Sorry I was typing my previous response from my phone. > > The snippet of code that I was talking about can be found at: > https://github.com/glouppe/scikit-learn/blob/master/sklearn/ensemble/forest.py#L93 > > Cheers, > > Gilles > > > On Wednesday, 26 September 2012, Gilles Louppe <[email protected]> wrote: >> Hi, >> >> The ensemble classes handle the problem you describe already. Have a look >> at the implementation of predict_proba of BaseForestClassifier in >> ensemble.py if you want to do that yourself by hand. >> >> Hope this helps. >> >> Gilles >> >> On Wednesday, 26 September 2012, Mathieu Blondel <[email protected]> >> wrote: >>> >>> >>> On Wed, Sep 26, 2012 at 3:52 AM, Doug Coleman <[email protected]> >>> wrote: >>>> >>>> If you examine the code, fit() "warms up" the optimization with some >>>> additional parameters, then calls _partial_fit(). partial_fit() just >>>> calls _partial_fit() directly. So, it looks like fit() and >>>> partial_fit() could take a `classes` parameter for SGDClassifier, >>>> rather than __init__. It seems a bit confused, actually, since >>>> SGDClassifier's __init__ takes a class_weight dict for doing >>>> cost-sensitive learning but then partial_fit() takes a classes >>>> vector--what if they contradict each other? >>> >>> partial_fit should behave exactly like fit if you call it only once. So, >>> for your use case, I would just use partial_fit with the classes parameter. >>> # The difference between fit and partial_fit is that fit erases the >>> previous model on subsequent calls whereas partial_fit starts from the >>> previous model. >>> Mathieu > > ------------------------------------------------------------------------------ > Live Security Virtual Conference > Exclusive live event will cover all the ways today's security and > threat landscape has changed and how IT managers can respond. Discussions > will include endpoint security, mobile security and the latest in malware > threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ How fast is your code? 3 out of 4 devs don\\\'t know how their code performs in production. Find out how slow your code is with AppDynamics Lite. http://ad.doubleclick.net/clk;262219672;13503038;z? http://info.appdynamics.com/FreeJavaPerformanceDownload.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
