Pruning a decision tree is a technique used to make it simpler and prevent it from becoming too complex. This can happen when the tree is tailored to the training data, making it perform poorly on new, unseen data. The goal of pruning is to simplify the tree structure by removing unnecessary branches while keeping its predictive power. There are two types of pruning: pre-pruning and post-pruning.

Pre-pruning, also known as early stopping, involves setting limits on the tree-building process. For example, you can limit the maximum depth of the tree, the minimum number of required samples to split a node or the minimum number of samples allowed in a leaf node. These limits prevent the tree from growing too deep or becoming too specific to the training data.

Post-pruning, also known as cost-complexity pruning, involves building the full tree and then removing branches that do not significantly improve predictive performance. The decision tree is grown without limits first, and then nodes are pruned based on a cost-complexity measure. This measure considers the accuracy of the tree and its size. Nodes that do not contribute sufficiently to accuracy are pruned to simplify the model.