I am practicing SVM in R using the iris dataset and I want to get the feature weights/coefficients from my model, but I think I may have misinterpreted something given that my output gives me 32 support vectors. I was under the assumption I would get four given I have four variables being analyzed. I know there is a way to do it when using the `svm()` function, but I am trying to use the `train()` function from caret to produce my SVM. 

```
library(caret)

# Define fitControl
fitControl <- trainControl(## 5-fold CV
              method = "cv",
              number = 5,
              classProbs = TRUE,
              summaryFunction = twoClassSummary )

# Define Tune
grid<-expand.grid(C=c(2^-5,2^-3,2^-1))

########## 
df<-iris head(df)
df<-df[df$Species!='setosa',]
df$Species<-as.character(df$Species)
df$Species<-as.factor(df$Species)

# set random seed and run the model
set.seed(321)
svmFit1 <- train(x = df[-5],
                 y=df$Species,
                 method = "svmLinear", 
                 trControl = fitControl,
                 preProc = c("center","scale"),
                 metric="ROC",
                 tuneGrid=grid )
svmFit1

```

I thought it was simply `svmFit1$finalModel@coef`but I get 32 vectors when I believe I should get 4. Why is that?