What is pruning in the context of decision trees?

Pruning in the context of decision trees refers to the technique of removing branches that have little significance or contribute minimally to the predictive power of the model. This process is vital for enhancing model performance and mitigating overfitting, where a tree becomes excessively complex and captures noise rather than the underlying trends in the data.

When branches and nodes that do not provide meaningful information are removed, the decision tree becomes simpler and more generalizable. This helps in improving the accuracy of predictions on unseen data by focusing on the most relevant features and decisions. Pruning leads to a more efficient model, reducing computational requirements and improving interpretability by displaying a clearer decision-making path.

The other options relate to modifying the structure of the tree in ways that do not align with the concept of pruning. Increasing the depth of the tree or expanding it adds complexity rather than simplifying it, while dividing data into more subsets also does not capture the essence of pruning, which is specifically about reducing and refining the existing structure of the decision tree.