Measuring the prediction performance of a recursive partitioning tree

Since we built a classification tree in the previous recipes, we can use it to predict the category (class label) of new observations. Before making a prediction, we first validate the prediction power of the classification tree, which can be done by generating a classification table on the testing dataset. In this recipe, we will introduce how to generate a predicted label versus a real label table with the predict function and the table function, and explain how to generate a confusion matrix to measure the performance.

Getting ready

You need to have the previous recipe completed by generating the classification model, churn.rp. In addition to this, you have to prepare the training dataset, trainset, and the testing dataset, testset, generated in the first recipe of this chapter.

How to do it...

Perform the following steps to validate the prediction performance of a classification tree:

1. You can use the predict function to...