Which of the following is NOT an advantage of regression trees over linear regression?

In the context of comparing regression trees and linear regression, it is important to recognize the strengths and weaknesses of each modeling approach. Regression trees are quite powerful and offer several advantages, including the ability to handle qualitative predictors and their intuitive structure which can often mimic human decision-making processes.

Focusing on the specified choice, stating that regression trees have a higher level of predictive accuracy is not inherently true across all cases. While regression trees can be highly accurate in certain datasets and circumstances, their performance heavily depends on the nature of the data, including its complexity and the presence of non-linearity. In many scenarios, especially with simpler datasets or linear relationships, linear regression may perform comparably or even better than a regression tree, especially when overfitting becomes a consideration with trees.

Thus, the statement regarding predictive accuracy is not a universal advantage of regression trees over linear regression, making it the correct choice for the question being examined.