Discuss the importance of data : Pruning a tree

The video discusses the challenges of large decision trees, such as difficulty in interpretation and overfitting, leading to poor test performance. It highlights the limitations of current strategies that stop tree growth based on predefined conditions, which can be shortsighted. To address this, tree pruning is introduced, where a large tree is pruned to form an optimal subtree with the lowest test error rate. The video explains cost complexity pruning, which adds a penalty for the number of terminal nodes to the RSS, controlled by a tuning parameter, alpha. This method helps find the optimal tree structure with minimal cross-validated error.

Describe the limitations of strategies that stop tree growth based on a predetermined condition.

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What is the significance of cross-validation in determining the optimal value of alpha?

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