What are the benefits of pruning decision trees?

What are the benefits of pruning decision trees?

Pruning decision trees helps reduce overfitting, improves model generalization, and enhances interpretability.

Can you explain how pruning reduces overfitting?

Sure! Pruning removes branches that have little importance, which helps the model focus on the most significant patterns.

Are there different methods of pruning?

Yes, common methods include cost complexity pruning, reduced error pruning, and minimum description length pruning.

How does pruning affect the accuracy of the model?

Pruning can improve accuracy on unseen data by simplifying the model, but it may slightly reduce training accuracy.

Is pruning always necessary for decision trees?

Not always, but it's often beneficial, especially for complex trees that may overfit the training data.