with
pred.pca<-predict(splits[[i]]$pca,trainingData@samples)[,1:nPCs]
dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2)));
results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc",
C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T)
and a degree of 5 i get an error of 0 reported by the ksvm object. But when
doing
pred.pca<-predict(splits[[i]]$pca,trainingData@samples)[,1:nPCs]
pred.svm<-kernlab::predict(results[[i]]$classifier,pred.pca,type="probabilities");
results[[i]]$trainResults$predicted<-pred.svm[,2]
the results vary widely from the class vector. Nearly all predictions are
somewhat around 0.29. Its just strange. And i have no idea where things go
wrong. They're in the same loop with i, so its probably not an indexing issue.
Maybe kernlabs predict doesn't scale the data or something?
______________________________________________
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.
