You are working with a Decision Tree that is computationally expensive to train. How might you leverage pruning to reduce the computational burden?

Applying pruning techniques like Reduced Error Pruning or Cost Complexity Pruning reduces the tree's complexity, leading to a less computationally expensive training process. These techniques aim to create a simpler model without significantly sacrificing performance.