Pruning techniques

Pruning techniques refer to methods used to simplify complex models, such as decision trees or neural networks, by removing less important parts. This helps improve performance, reduce overfitting, and make the model more efficient. In decision trees, pruning cuts branches that don't add much value, while in neural networks, it removes unnecessary connections. Think of it like trimming a tree to encourage healthier growth; pruning keeps the model focused on the most relevant features, leading to more accurate and faster predictions.