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If a decision tree used for optimizing the route of delivery trucks has high information gain at each split, which consequence follows?

A)Increased computational cost during deployment
B)Reduced interpretability of the model
C)The model exhibits high bias
D)Faster and more accurate route optimization

💡 Explanation

High information gain means each split significantly reduces uncertainty in route optimization, because the information gain splitting criteria selects features that best separate the data, therefore the routing becomes faster and more accurate, rather than leading to increased cost or bias.

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