I will look into that, thanks. I am afraid I don't quite understand what is
going on there with the multiplication, so I will need to read up. What I
ended up doing was like so:
For train data, its easy, as I can subset to have the model only work off the
data I want:
rbfSVM_setosa <- train(Sepal.Length~., data = trainset, subset =
trainset$Species=="setosa", ...)
rbfSVM_versicolor <- train(Sepal.Length~., data = trainset, subset =
trainset$Species=="versicolor", ...)
For my test data (testset), I ended up doing like so which appears to work:
index_setosa<- which(testset$Species == "setosa")
svmPred <- as.vector(rep(NA,nrow(testset)))
svmPred[index_setosa] <- predict(rbfSVM_setosa, testset[testset$Species ==
"setosa",])
svmPred[is.na(svmPred)] <- predict(rbfSVM_versicolor, testset[testset$Species
== "versicolor",])
The above works when there are just two classes. I am going to read on some of
these other ways suggested and give them a try.
Brian
On Dec 10, 2012, at 10:38 PM, Thomas Stewart <tgs.public.m...@gmail.com> wrote:
> Why not use an indicator variable?
>
> P1 <- ... # prediction from model 1 (Setosa) for entire dataset
>
> P2 <- ... # prediction from model 2 for entire dataset
>
> I <- Species=="setosa" #
>
> Predictions <- P1 * I + P2 * ( 1 - I )
>
> On Monday, December 10, 2012, Brian Feeny wrote:
>
> I have a dataset and I wish to use two different models to predict. Both
> models are SVM. The reason for two different models is based
> on the sex of the observation. I wish to be able to make predictions and
> have the results be in the same order as my original dataset. To
> illustrate I will use iris:
>
> # Take Iris and create a dataframe of just two Species, setosa and
> versicolor, shuffle them
> data(iris)
> iris <- iris[(iris$Species=="setosa" | iris$Species=="versicolor"),]
> irisindex <- sample(1:nrow(iris), nrow(iris))
> iris <- iris[irisindex,]
>
> # Make predictions on setosa using the mySetosaModel model, and on versicolor
> using the myVersicolorModel:
>
> predict(mySetosaModel, iris[iris$Species=="setosa",])
> predict(myVersicolorModel, iris[iris$Species=="versicolor",])
>
> The problem is this will give me a vector of just the setosa results, and
> then one of just the versicolor results.
>
> I wish to take the results and have them be in the same order as the original
> dataset. So if the original dataset had:
>
>
> Species
> setosa
> setosa
> versicolor
> setosa
> versicolor
> setosa
>
> I wish for my results to have:
> <prediction for setosa>
> <prediction for setosa>
> <prediction for versicolor>
> <prediction for setosa>
> <prediction for versicolor>
> <prediction for setosa>
>
> But instead, what I am ending up with is two result sets, and no way I can
> think of to combine them. I am sure this comes up alot where you have a
> factor you wish to split your models on, say sex (male vs. female), and you
> need to present the results back so it matches to the order of the orignal
> dataset.
>
> I have tried to think of ways to use an index, to try to keep things in
> order, but I can't figure it out.
>
> Any help is greatly appreciated.
>
> Brian
>
> ______________________________________________
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>
>
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