Society of Actuaries (SOA) PA Practice Exam

Which of the following is a consequence of using Boosted Trees?

1. They often reduce bias but increase variance.

2. They typically have a quicker training time than Random Forests.

3. They learn more slowly than other tree methods.

4. They do not require variable importance measures.

The correct answer is: They learn more slowly than other tree methods.

Using Boosted Trees leads to a significant improvement in predictive performance, particularly in complex datasets. They are designed to build models sequentially; each new tree corrects errors made by previously trained trees. This characteristic allows Boosted Trees to refine their predictions iteratively, leading to a model that often has lower bias compared to simpler methods. However, this process involves a more meticulous approach in terms of computation and time because new trees take the errors of prior trees into account. While it may sound like they learn at a slower pace because they are building one tree at a time, this fosters a robust learning environment that can capture intricate relationships in the data. Consequently, this meticulous nature can lead to longer overall training times, contrasting with methods like Random Forests, where trees are constructed independently. In essence, while Boosted Trees might seem slower in terms of learning due to their sequential nature and individual correction of errors, they ultimately provide a refined and efficient prediction capability that addresses complex relationships within the data. Thus, the statement regarding their learning speed accurately reflects this comparative aspect to other tree methods.