Just curious, once you have a model that works well, does it make sense to then tune it against 100% of the dataset (with known outcomes) so you can apply it to data you wish to predict for or is that a bad approach?
I have done like is explained in this thread many times, taken a sample, learned against it, and then tested on the remaining. But this is using data for which we know the predicted variable and can compare to validate. So after your done, should you re-tune with the entire training set? As for which method, I am using mostly SVM Brian On Nov 19, 2012, at 2:07 PM, Eddie Smith <eddie...@gmail.com> wrote: > Thanks a lot! I got some ideas from all the replies and here is the final one. > > newdata > > select <- sample(nrow(newdata), nrow(newdata) * .7) > data70 <- newdata[select,] # select > write.csv(data70, "data70.csv", row.names=FALSE) > > data30 <- newdata[-select,] # testing > write.csv(data30, "data30.csv", row.names=FALSE) > > Cheers > > ______________________________________________ > 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-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.
