using "foreach" for running different classifiers in R -

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i trying use foreach run different classifiers on data, doesn't work. in fact doesn't return me anything.
purpose parallelize process. here simplified of code: 

library(foreach) library(doparallel) no_cores <- detectcores() - 1 cl<-makecluster(no_cores) registerdoparallel(cl) registerdoparallel(no_cores)  model_list<-foreach(i = 1:2,                .combine = c,.packages=c("e1071","randomeforest"))  %dopar%     if (i==1){     model1<-svm(x = x,y = as.factor(y),type = "c-classification",probability = t)   }   if (i==2){     mode2<-randomforest(x = x,y = as.factor(y), ntree=100, norm.votes=false,importance = t) }

my way of parallelizing correct overall?
indeed.

the main problem you're not enclosing body of foreach loop in curly braces. because %dopar% binary operator, have careful precedence, why recommend using curly braces.

also, shouldn't use c combine function. since svm , randomforest return objects, default behavior of returning results in list appropriate. combining them c give garbage result.

finally, doesn't make sense call registerdoparallel twice. doesn't hurt, makes code confusing.

i suggest:

library(doparallel) no_cores <- detectcores() - 1 registerdoparallel(no_cores)  model_list <- foreach(i = 1:2,               .packages=c("e1071","randomforest"))  %dopar% {   if (i==1) {     svm(x = x,y = as.factor(y),type = "c-classification",        probability = t)   } else {     randomforest(x = x,y = as.factor(y), ntree=100, norm.votes=false,                  importance = t)   } }

i removed 2 unnecessary variable assignments model1 , model2. variables won't defined correctly on master, , obscures how foreach loop works.


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