To measure the performance of a classification model, we can first generate a classification table based on our predicted label and actual label. Then, we can use a confusion matrix to obtain performance measures such as precision, recall, specificity, and accuracy. In this recipe, we will demonstrate how to retrieve a confusion matrix using the caret
package.
In this recipe, we will continue to use the telecom churn
dataset as our example dataset.
Perform the following steps to generate a classification measurement:
- Train an svm model using the training dataset:
> svm.model= train(churn ~ ., + data = trainset, + method = "svmRadial")
- You can then predict labels using the fitted model,
svm.model
:
> svm.pred = predict(svm.model, testset[,! names(testset) %in% c("churn")])
- Next, you can generate a classification table:
>...