Similar to bagging.cv, we can perform cross-validation with the boosting method using boosting.cv. If v is set to 10 and mfinal is set to 5, the boosting method will perform 10-fold cross-validations with five iterations. Also, one can set the control of the rpart fit within the parameter. We can set the complexity parameter to 0.01 in this example. Once the training is complete, the confusion matrix and average errors of the boosted results will be obtained.