tree pruning techniques

Tree pruning techniques involve trimming or removing parts of a decision tree to improve its accuracy and prevent overfitting. This process simplifies the model by cutting branches that contribute little to prediction accuracy, making the tree smaller and more generalizable to new data. Techniques include pre-pruning, where you stop the tree from growing too complex during training, and post-pruning, which involves trimming branches after the tree is fully grown. Pruning helps in making the model easier to interpret, reduces errors on new data, and enhances overall performance by focusing on the most important decision points.