An automated lumber mill uses decision trees to classify wood. Which outcome occurs when a decision tree excessively branches in classifying lumber?

💡 Explanation

Excessive branching in a decision tree leads to overfitting, because the model learns noise and irrelevant details in the training data, therefore it performs poorly on unseen data; rather than generalizing well, it memorizes training examples.

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