Question

5. Why might an analyst choose to use CART rather than random forests if the latter is better at preventing overfitting?

Answer 1

CART (Classification and Regression Trees) may be chosen over random forests for reasons such as simplicity, interpretability, and the focus on exploration and hypothesis generation.

Step-by-step explanation:

Even though random forests are superior at preventing overfitting, an analyst may decide to use CART (Classification and Regression Trees). One explanation is that, in contrast to random forests, CART is more straightforward and understandable. It offers a single decision tree that is easier to comprehend and evaluate. Furthermore, if the dataset is small or if generating and exploring hypotheses is more important than obtaining the best predictive accuracy, CART might be the better option.