How does pruning affect a decision tree model?

Pruning is a technique used in decision tree models to reduce overfitting, which occurs when a model learns the noise in the training data rather than the underlying data distribution. By removing certain branches of the tree that add little predictive power, pruning helps to create a simpler model that focuses on the most relevant features of the data.

This simplification facilitates better generalization to unseen data, ultimately enhancing the model's performance. A pruned tree typically has fewer splits and nodes, which makes it easier to interpret while simultaneously ensuring that the model maintains or improves its predictive accuracy on new data instances. Therefore, engaging in pruning can indeed lead to a model that performs better overall by avoiding the pitfalls of overfitting frequently associated with more complex models.