What is pruning in machine learning and how does it help with overfitting?

What is pruning in machine learning and how does it help with overfitting?

Pruning is a technique used to reduce the size of a machine learning model by removing parts that are not necessary. This helps to prevent overfitting, which occurs when a model learns noise in the training data instead of the actual patterns.

Can you explain how pruning is implemented in decision trees?

In decision trees, pruning can be done by removing branches that have little importance. This can be achieved through methods like cost complexity pruning, where you balance the tree's accuracy with its complexity.

Are there different types of pruning methods?

Yes, there are several types of pruning methods, including pre-pruning, post-pruning, and reduced error pruning. Each has its own approach to managing model complexity.

How can I tell if my model is overfitting?

You can identify overfitting by comparing the performance of your model on training and validation datasets. If the training accuracy is high but the validation accuracy is low, your model is likely overfitting.

What are some other techniques to prevent overfitting?

Other techniques include regularization, dropout in neural networks, and using more training data to improve generalization.