In a recommendation system using a decision tree, which outcome arises if the splitting criteria maximizes information gain without considering tree size?

💡 Explanation

Without pruning, the decision tree will grow excessively to perfectly classify the training data, because maximizing information gain at each split leads to capturing noise and irrelevant details. Therefore, the model overfits, rather than generalizes well to new data, as pruning techniques would prevent this.

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