SVM with polynomial kernel
In this exercise you will build a SVM with a quadratic kernel (polynomial of degree 2) for the radially separable dataset you created earlier in this chapter. You will then calculate the training and test accuracies and create a plot of the model using the built in plot()
function. The training and test datasets are available in the dataframes trainset
and testset
, and the e1071
library has been preloaded.
This exercise is part of the course
Support Vector Machines in R
Exercise instructions
- Build SVM model on the training data using a polynomial kernel of degree 2.
- Calculate training and test accuracy for the given training/test partition.
- Plot the model against the training data.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
svm_model<-
svm(y ~ ., data = trainset, type = "C-classification",
kernel = ___, degree = ___)
#measure training and test accuracy
pred_train <- predict(svm_model, ___)
mean(pred_train == ___$y)
pred_test <- predict(svm_model, ___)
mean(pred_test == ___$y)
#plot
plot(___, trainset)