Now, you will try an SVM using a radial basis function $($RBF$).$ RBF should allow you to capture the non$-$linearity in the data. To create the specification, you should use svm$\_$rbf$().$ Be sure to pass in classification as the mode and kernlab as the engine. Save your output to svm$\_$rbf$\_$spec.

# YOUR CODE HERE

set.seed$(1)$

# svm$\_$rbf$\_$spec $<-$

# your code here

$$

svm$\_$rbf$\_$spec $<-$ svm$\_$rbf$()\%>\%$

set$\_$mode$("$classification$")\%>\%$

set$\_$engine$("$kernlab$",$ scaled $=$ FALSE$)$

$$

Now fit your model using fit$().$

# your code here

svm$\_$rbf$\_$fit$=$ function$()\{$

fit$($svm$\_$rbf$\_$spec, data $=$ sim$\_$data$2)$

$\}$

$$

Plot your model. What do you notice?

library$($kernlab$)$

$$

# YOUR CODE HERE

fit$\_$plot $<-$ plot$($svm$\_$rbf$\_$fit$)$

fit$\_$plot

# fit$\_$plot $<-$

$$

# your code here

$$