A decision tree classifies network packets to detect intrusions; which outcome is most likely if the Information Gain splitting criterion excessively favors attributes with many values?

💡 Explanation

When Information Gain overly favors attributes with many values, the decision tree becomes highly specialized to the training data, because this results in overfitting. The tree captures noise rather than generalizable patterns; therefore, accuracy decreases on new data rather than increasing, as would occur with a well-generalized tree.

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