Hi Amir. 1) As far as I know, the gradient boosting works only with trees using deviance or least squares regression. I don't think it should be hard to add other losses, though.
2) There are at the moment no plans to add structured SVMs to the library. The reason is that structured models usually are very problem specific. It is possible to build generic frameworks like Joachsim SVMstruct, which works by the user specifying functions for features, inference and loss-augmented inference, but this doesn't really fit well with the sklearn principle of using only arrays as data structures and having a simple "fit/predict" interface. What application did you have in mind? In general I would love to have structured learning in sklearn, it just seems hard to integrate nicely. Btw, I have some structured SVM code to play around in Python, if you want: http://peekaboo-vision.blogspot.co.uk/2012/06/structured-svm-and-structured.html Cheers, Andy ----- Ursprüngliche Mail ----- Von: "amir rahimi" <[email protected]> An: [email protected] Gesendet: Mittwoch, 8. August 2012 12:40:52 Betreff: [Scikit-learn-general] GradientBoostingRegression loss function and Structured svm Hi all, I have two questions/requests Is there any way to define arbitrary loss function for gradient boosting regression? e.g. using huber penalty My request is about adding structured output prediction for SVM in the library. Is there any plan for adding that? -- ---------------------------------------------------------------------- #include <stdio.h> double d[]={9299037773.178347,2226415.983937417,307.0}; main(){d[2]--?d[0]*=4,d[1]*=5,main():printf((char*)d);} ---------------------------------------------------------------------- ------------------------------------------------------------------------------ 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 ------------------------------------------------------------------------------ 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
