A decision tree used for classifying web server traffic encounters high variance during real-time predictions. Which consequence follows from aggressively pruning the tree?

💡 Explanation

Aggressive pruning reduces the tree's complexity, mitigating overfitting and improving its ability to generalize to new, unseen data, because the tree fits the essential patterns rather than noise. Therefore, generalization improves; rather than other options, such as underfitting, which occur with excessive simplification.

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