The predict() function can be used to predict the class label on a set of test observations, at any given value of the cost parameter. We begin by generating a test data set.

xtest <- matrix(rnorm(20 * 2), ncol = 2)
ytest <- sample(c(-1, 1), 20, rep = TRUE)
xtest[ytest == 1,] <- xtest[ytest == 1,] + 1
testdat <- data.frame(x = xtest, y = as.factor(ytest))

Now we predict the class labels of these test observations. Here we use the best model obtained through cross-validation in order to make predictions.

ypred <- predict(bestmod, testdat)
table(predict = ypred, truth = testdat$y)

       truth
predict -1 1
     -1  9 1
     1   2 8

Thus, with this value of cost, 17 of the test observations are correctly classified.

Questions

• Create a model with a linear kernel, unscaled variables and a cost of 0.01. Store this model in svmfit
• predict the classes of the test data with this model and store the predictions in ypred.
• Store the confusion matrix on the test data in cf.test
  (note: give the predictions as the first argument in the table() function, and the ground truth as the second argument).

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Assume that:

• The e1071 library has been loaded