Pruning a decision tree is a strategy employed to simplify its structure and prevent it from becoming overly intricate, leading to suboptimal performance on new, unseen data. The core objective of pruning is to streamline the tree by removing unnecessary branches while retaining its predictive capabilities. There are two main pruning methods: pre-pruning and post-pruning.
Pre-pruning, also known as early stopping, entails placing constraints during the tree-building process. This can involve limiting the tree’s maximum depth, specifying the minimum number of samples required to split a node, or setting a threshold for the minimum number of samples allowed in a leaf node. These limitations act as safeguards to prevent the tree from growing excessively complex or becoming too specific to the training data.
In contrast, post-pruning, or cost-complexity pruning, involves initially constructing the full tree and then eliminating branches that contribute minimally to improving predictive performance. The decision tree is allowed to grow without restrictions initially, and subsequently, nodes are pruned based on a cost-complexity measure that considers both the accuracy of the tree and its size. Nodes that do not significantly enhance accuracy are pruned, simplifying the overall model.