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.
You need to have completed the previous recipe by storing the fitted bagging model in the variable, churn.bagging
. Also, put the fitted boosting classifier in churn.boost
.
Perform the following steps to calculate the error evolution of each ensemble learner:
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", + ylab = "Error", col = "red", lwd = 2...