Which outcome occurs when a decision tree, used for image classification, splits nodes based solely on maximizing information gain without any pruning?

💡 Explanation

Overfitting develops because the decision tree excessively learns noise from the training set through the Information Gain mechanism without regularization from pruning; therefore, the tree fits the training data too closely, rather than generalizing to new data like pruning would allow.

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