What is the term for the process of simplifying a decision tree by removing nodes, branches, and leaves that offer little value?

The process of simplifying a decision tree by removing nodes, branches, and leaves that offer little value is known as pruning. This technique is crucial in decision tree modeling as it helps reduce overfitting, which occurs when the model is too complex and captures noise in the training data rather than the underlying pattern. By pruning, the tree becomes less complex and more generalizable to unseen data, improving its performance and interpretability.

Pruning typically focuses on elements that do not significantly contribute to the accuracy or predictive power of the model. As a result, the final decision tree is more efficient, requires less memory, and can make quicker predictions while maintaining a good level of accuracy. This technique is a well-established part of model optimization in machine learning practices.