What is decision tree pruning and why is it important?

What is decision tree pruning and why is it important?

Decision tree pruning is a technique used to reduce the size of a decision tree by removing sections of the tree that provide little power in predicting target variables. It's important because it helps to prevent overfitting, making the model more generalizable to unseen data.

How does pruning affect the accuracy of a decision tree?

Pruning can improve the accuracy of a decision tree on new data by simplifying the model. A pruned tree is less complex and focuses on the most significant features, which can lead to better performance on unseen datasets.

What are the different methods of pruning?

There are several methods of pruning, including pre-pruning (stopping the tree from growing too deep) and post-pruning (removing branches after the tree has been fully grown). Common techniques include cost complexity pruning and reduced error pruning.

Can you give an example of when to use pruning?

Pruning is particularly useful when you have a very deep decision tree that performs well on training data but poorly on validation data. For instance, in a dataset with many features and limited samples, pruning can help create a more robust model.