You use tune function to find optimal parameters needed for particular classification algorithm. I had more experience with tune.svm but, I would try first to put parameters covering the whole possible range of each variable (in which algorithm do not crash), for example c(4^-2, 4^-1, 4^0, 4^1, 4^2) look at the results and than narrow down the search in the best ranges. For allowed ranges of parameters you will need to experiment or study documentation of your chosen classifier (nnet).
Jarek Tuszynski -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of madhurima bhattacharjee Sent: Friday, March 10, 2006 5:38 AM To: r-help@stat.math.ethz.ch; Bioconductor Subject: [R] need help in tune.nnet Dear R people, I want to use the tune.nnet function of e1071 package to tune nnet . I am unable to understand the parameters of tune.nnet from the e1071 pdf document. I have performed nnet on a traindata and want to test it for class prediction with a testdata. I want to know the values of size,decay,range etc. parameters for which the prediction of testdata is best. Can anyone please tell me how to do this with the tune.nnet function. Anticipating quick response. Thanks and Regards, Madhurima. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
