What is the main advantage of using random forest (RF) over a single decision tree (DT) in a classification task? RF can avoid overfitting better than a single DT of same depth RF can achieve higher accuracy than a single DT only when the number of trees is very large RF is computationally faster than a single DT of same depth RF can handle missing values better than a single decision tree