In this recipe, we use a recursive partitioning tree from the rpart package to build a tree-based classification model. The recursive portioning tree includes two processes: recursion and partitioning. During the process of decision induction, we have to consider a statistic evaluation question (or simply a yes/no question) to partition the data into different partitions in accordance with the assessment result. Then, as we have determined the child node, we can repeatedly perform the splitting until the stop criteria is satisfied.
For example, the data (shown in the following figure) in the root node can be partitioned into two groups with regard to the question whether f1 is smaller than X. If so, the data is divided into ...
