Caret - Scaling SVM tuning parametert (Sigma) when using plot.train

Question

I am using the Caret package to tune a SVM model.

Is there a way to scale the Sigma values similar to the Cost values when plotting the results (as shown in the attached Fig.).

Here is my tuning values:

svmGrid <- expand.grid(sigma= 2^c(-25, -20, -15,-10, -5, 0), C= 2^c(0:5))

Code to produce the plot:

pdf("./Figures/svm/svmFit_all.pdf", width=7, height = 5)
trellis.par.set(caretTheme())
plot(svmFit.all, scales = list(x = list(log = 2)))
dev.off()

Thanks

topepo topepo · Accepted Answer · 2015-08-28T19:03:43

You would have to do it yourself via lattice:

library(caret)

set.seed(1345)
dat <- twoClassSim(2000)

svmGrid <- expand.grid(sigma= 2^c(-25, -20, -15,-10, -5, 0), C= 2^c(0:5))

set.seed(45)
mod <- train(Class ~ ., data = dat, 
             method = "svmRadial",
             preProc = c("center", "scale"),
             tuneGrid = svmGrid,
             metric = "ROC",
             trControl = trainControl(method = "cv", 
                                      classProbs = TRUE, 
                                      summaryFunction = twoClassSummary))

tmp <- mod$results
tmp$sigma2 <- paste0("2^", format(log2(tmp$sigma)))

xyplot(ROC ~ C, data = tmp, 
       groups = sigma2,
       type = c("p", "l"),
       auto.key = list(columns = 4, lines = TRUE),
       scales = list(x = list(log = 2)),
       xlab = "Cost", 
       ylab = "ROC (Cross-Validation)")

Max

Caret - Scaling SVM tuning parametert (Sigma) when using plot.train

1 Answers