We need to prune the tree in such a way to reduce the prediction error rate. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. The dual goal of pruning is the reduction complexity of the final classifier as well as better predictive accuracy by the reduction of over-fitting and removal of sections of a classifier that may be based on noisy or erroneous data.