Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to predict the labels of the testing dataset:

1. Predict the label of the testing dataset based on the fitted SVM and attributes of the testing dataset:

        > svm.pred = predict(model, testset[, !names(testset)         %in% c("churn")])

2. Then, you can use the table function to generate a classification table with the prediction result and labels of the testing dataset:

        > svm.table=table(svm.pred, testset$churn)        > svm.table        Output        svm.pred yes no        yes 70 12            no 71 865

3. Next, you can use classAgreement to calculate coefficients compared to the classification agreement:

        > classAgreement(svm.table)        Output         $diag         [1] 0.9184676               $kappa        [1] 0.5855903        $rand        [1] 0.850083        $crand        [1] 0.5260472

4. Now, you can use confusionMatrix ...