How does pruning affect model accuracy?

What is pruning in machine learning?

Pruning is a technique used to reduce the size of a machine learning model by removing unnecessary parameters or nodes.

How does pruning impact the accuracy of a model?

Pruning can improve model accuracy by reducing overfitting, but if done excessively, it may lead to underfitting.

Can you give an example of pruning in decision trees?

Sure! In decision trees, pruning involves removing branches that have little importance, which helps in simplifying the model and improving generalization.

Is there a way to determine the optimal level of pruning?

Yes, techniques like cross-validation can help determine the optimal level of pruning by evaluating model performance on unseen data.