Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to prune the classification tree:

1. Find the minimum cross-validation error of the classification tree model:

        > min(churn.rp$cptable[,"xerror"])
        Output    
        [1] 0.4707602  

2. Locate the record with the minimum cross-validation errors:

        > which.min(churn.rp$cptable[,"xerror"])
        Output
        7  

3. Get the cost complexity parameter of the record with the minimum cross-validation errors:

        > churn.cp = churn.rp$cptable[7,"CP"]
        > churn.cp
        Output
        [1] 0.01754386  

4. Prune the tree by setting the cp parameter to the CP value of the record with minimum cross-validation errors:

        > prune.tree = prune(churn.rp, cp= churn.cp)

5. Visualize the classification tree by using the plot and text function:

 > plot(prune.tree, margin= ...