Calculating the error evolution of the ensemble method

The adabag package provides the errorevol function for a user to estimate the ensemble method errors in accordance with the number of iterations. In this recipe, we will demonstrate how to use errorevol to show the evolution of errors of each ensemble classifier.

Getting ready

You need to have completed the previous recipe by storing the fitted bagging model in the churn.bagging variable. Also, put the fitted boosting classifier in churn.boost.

How to do it...

Perform the following steps to calculate the error evolution of each ensemble learner:

1. First, use the errorevol function to calculate the error evolution of the boosting classifiers:

        > boosting.evol.train = errorevol(churn.boost, trainset)
        > boosting.evol.test = errorevol(churn.boost, testset)
        > plot(boosting.evol.test$error, type = "l", ylim = c(0, 1),
        +       main = "Boosting error versus number of trees", xlab =
        "Iterations",
     ...