Perform the following steps to generate a classification model for the telecom churn dataset:

1. First, you need to install and load the adabag package (it might take a while to install adabag):

        > install.packages("adabag")
        > library(adabag)  

2. Next, you can use the bagging function to train a training dataset (the result may vary during the training process):

        > set.seed(2)
        > churn.bagging = bagging(churn ~ ., data=trainset, mfinal=10)      

3. Access the variable importance from the bagging result:

         > churn.bagging$importance
        Output:
           international_plan number_customer_service_calls 
              10.4948380           16.4260510 
        number_vmail_messages total_day_calls 
               0.5319143           0.3774190 
             total_day_charge total_day_minutes 
               0.0000000          28.7545042 
              total_eve_calls total_eve_charge 
               0.1463585           0.0000000 
            total_eve_minutes total_intl_calls 
              14.2366754           8.7733895 
            total_intl_charge total_intl_minutes 
               0.0000000           9.7838256 
            total_night_calls total_night_charge 
               0.4349952           0.0000000 
          total_night_minutes voice_mail_plan 
               2.3379622           7.7020671 

4. After generating the classification model, you can use the predicted results from the testing dataset:

        > churn.predbagging= predict.bagging(churn.bagging,
        newdata=testset) 

5. From the predicted results, you can obtain a classification table:

        > churn.predbagging$confusion
        Output:

                   Observed Class
        Predicted Class yes no
                    no 35 866
                    yes 106 11

6. Finally, you can retrieve the average error of the bagging result:

        > churn.predbagging$error
        Output
        [1] 0.0451866