A classification tree uses a split condition to predict class labels based on one or multiple input variables. The classification process starts from the root node of the tree; at each node, the process will check whether the input value should recursively continue to the right or left sub-branch according to the split condition, and stops when meeting any leaf (terminal) nodes of the decision tree. In this recipe, we will introduce how to apply a recursive partitioning tree on the customer churn dataset.
You need to have completed the previous recipe by splitting the churn dataset into the training dataset (
trainset) and testing dataset (
testset), and each dataset should ...