Which outcome occurs when a decision tree's 'information gain' metric favors splitting on attributes with numerous values, leading to overfitting on a manufacturing defect dataset?

💡 Explanation

Enhanced sensitivity to noisy data occurs because high information gain from numerous values causes the decision tree to fit the training data too closely, including its noise, which is known as overfitting; therefore, the model performs poorly on new, unseen data, rather than generalizing well like a pruned tree.

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