Visualizing decision & margin bounds using `plot()`
In this exercise, you will rebuild the SVM model (as a refresher) and use the built in SVM plot()
function to visualize the decision regions and support vectors. The training data is available in the dataframe trainset
.
This exercise is part of the course
Support Vector Machines in R
Exercise instructions
- Load the library needed to build an SVM model.
- Build a linear SVM model using the training data.
- Plot the decision regions and support vectors.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
#load required library
library(___)
#build svm model
svm_model<-
svm(y ~ ., data = ___, type = "C-classification",
kernel = "___", scale = FALSE)
#plot decision boundaries and support vectors for the training data
plot(x = svm_model, data = ___)