[R] interpretation of svm models with the e1071 package

[manuel.martin]

Dear all,

after having calibrated a svm model through the svm() command of the 
e1071 package, is there a way to
i) represent the modeled relationships between the y and X variables 
(response variable vs. predictors)?

ii) rank the influence of the predictors used in the model?

Right now I am more interested in regression models, but I guess this 
would be useful for classification too.


Thank you in advance,  manuel


--


INRA - InfoSol
Centre de recherche d'Orléans
2163 Avenue de la Pomme de Pin
CS 40001 ARDON
45075 ORLEANS Cedex 2
tel : (33) (0)2 38 41 48 21
fax : (33) (0)2 38 41 78 69
http://www.gissol.fr
http://bdat.orleans.inra.fr
00--

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: [R] interpretation of svm models with the e1071 package

[manuel.martin]

Thanks a lot for the reply, some comments below

On 07/10/2010 04:11 AM, Steve Lianoglou wrote:

Hi,

On Fri, Jul 9, 2010 at 12:15 PM, manuel.martin
  wrote:
   

Dear all,

after having calibrated a svm model through the svm() command of the e1071
package, is there a way to
i) represent the modeled relationships between the y and X variables
(response variable vs. predictors)?
 

Can you explain a bit more ... how do you want them represented?
   
I was thinking to a simple ŷ = fi(Xi) plot, fi resulting from the fitted 
svm model. Xi is the predictor, among the whole set of predictors, X, 
one wish to see the relationship with the response.
For boosted regression trees, which I am more familiar with, this is fi 
function is estimated by averaging the effects of all predictors but Xi, 
and plotting how ŷ varies as Xi does.


Hope this is a bit clearer, Manuel

   

ii) rank the influence of the predictors used in the model?
 

One technique that's often/sometimes used is to calculate the SVM's W
vector by using the support vectors along with their learned
weights/alphas.

This comes up every now and again. Here's an older post explaining how
you might do that with the svm model from e1071:

http://article.gmane.org/gmane.comp.lang.r.general/158272/match=w+b+vector+svr

Hope that helps.

   



--


INRA - InfoSol
Centre de recherche d'Orléans
2163 Avenue de la Pomme de Pin
CS 40001 ARDON
45075 ORLEANS Cedex 2
tel : (33) (0)2 38 41 48 21
fax : (33) (0)2 38 41 78 69
http://www.gissol.fr
http://bdat.orleans.inra.fr
00--

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

[R] book about "support vector machines"

[manuel.martin]

Dear all,
I am currently looking for a book about support vector machines for 
regression and classification and am a bit lost since they are plenty of 
books dealing with this subject. I am not totally new to the field and 
would like to get more information on that subject for later use with 
the e1071  
package for instance. Does anyone has an idea of a good book for that 
matter?

Thank you in advance and apologizes for posting a not totally R specific 
question.

Manuel

-- 


INRA - InfoSol
Centre de recherche d'Orléans
2163 Avenue de la Pomme de Pin
CS 40001 ARDON
45075 ORLEANS Cedex 2
tel : (33) (0)2 38 41 48 21
fax : (33) (0)2 38 41 78 69
http://www.gissol.fr
http://bdat.orleans.inra.fr
00--


[[alternative HTML version deleted]]

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.