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 (give the predictions as the first argument in the table() function with name predict, and the ground truth as the second argument with name truth).

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Assume that:

• The e1071 library has been loaded