Response:  When branches in a decision tree have weak predictive power, they are removed to reduce the complexity of the model or solution. They also increase the predictive accuracy of a decision tree. This is referred to as pruning on decision trees which are basic tree-based approaches used in machine learning.  Reduced error pruning is one of the simple methods that replace each node. This is used for optimizing the accuracy of the model/solution.  An unpruned decision tree example:  A pruned decision tree example:   A pruned tree has fewer nodes and less sparsity compared to an unpruned tree.