Pruning

The video tutorial explains the concept of pruning in both gardening and decision trees. It highlights how decision trees can grow large with unnecessary branches, affecting performance. Pruning, by removing low-value branches, can improve accuracy and efficiency. The tutorial covers two types of pruning: pre-pruning, which stops unnecessary branching early, and post-pruning, which removes branches after the tree is complete.

What is the primary reason for pruning a decision tree in machine learning?

• To make the tree look aesthetically pleasing

• To ensure the tree memorizes the training data

• To reduce the size and improve the accuracy of the tree

• To increase the number of branches

How does pruning affect the performance of a decision tree?

• It reduces the tree's accuracy

• It makes the tree slower

• It improves the tree's performance by removing unnecessary branches

• It increases the complexity of the tree

Which type of pruning involves analyzing and removing branches after the tree is fully built?

• Pre-pruning

• Root pruning

• Mid-pruning

• Post-pruning

What is the main goal of pre-pruning in decision trees?

• To stop the tree from branching unnecessarily

• To add more branches to the tree

• To memorize the training data

• To ensure the tree is fully grown before pruning

In the context of decision trees, what does post-pruning aim to achieve?

• To prevent the tree from growing

• To ensure the tree is specific to the training data

• To remove weak branches after the tree is complete

• To add complexity to the tree