Which of the following is a post$-$pruning strategy for decision trees to avoid model overfitting?

A$.$

Removing branches through random selection till desired tree size is achieved

B$.$

Removing branches below maximal depth of the decision tree

C$.$

Removing those branches that do not effectively change the classification error rates

D$.$