Cost complexity pruning

Cost complexity pruning

• A. Cost complexity pruning
• B. Pre-Pruning
• C. Ensemble Techniques
• D. Bootstrap aggregation

Answer: (A)

Cost complexity pruning is a method for trimming a decision tree to balance fit with simplicity. After a tree is grown, you evaluate how much complexity is really needed to explain the data by introducing a penalty for each additional terminal node. The goal is to minimize a cost that combines the tree’s misfit on the training data with a penalty proportional to its size. This leads to a sequence of subtrees, produced by pruning away the least impactful branches first, controlled by a parameter alpha that governs the trade-off between accuracy and simplicity. By testing these subtrees on a validation set or via cross-validation, you pick the version that generalizes best, avoiding overfitting. This approach differs from pre-pruning, which stops growth early during tree construction rather than refining an already grown tree. It also contrasts with ensemble techniques like bagging or bootstrap aggregation, which combine multiple models rather than pruning a single tree.

Cost complexity pruning is a method for trimming a decision tree to balance fit with simplicity. After a tree is grown, you evaluate how much complexity is really needed to explain the data by introducing a penalty for each additional terminal node. The goal is to minimize a cost that combines the tree’s misfit on the training data with a penalty proportional to its size. This leads to a sequence of subtrees, produced by pruning away the least impactful branches first, controlled by a parameter alpha that governs the trade-off between accuracy and simplicity. By testing these subtrees on a validation set or via cross-validation, you pick the version that generalizes best, avoiding overfitting.

This approach differs from pre-pruning, which stops growth early during tree construction rather than refining an already grown tree. It also contrasts with ensemble techniques like bagging or bootstrap aggregation, which combine multiple models rather than pruning a single tree.