Society of Actuaries (SOA) PA Practice Exam

What characterizes a Decision Tree model?

1. It is a linear regression technique.

2. It relies on complex mathematical equations.

3. It uses a set of if/then rules derived from data features.

4. It requires a continuous output variable only.

The correct answer is: It uses a set of if/then rules derived from data features.

A Decision Tree model is characterized by its use of a set of if/then rules that are derived from the features in the data. This model structures decisions and their possible consequences in a tree-like format, where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents an outcome or prediction. The process involves splitting the data into subsets based on feature values, allowing the model to capture interactions and non-linear relationships between the features effectively. The other options describe characteristics that do not align with the principles of a Decision Tree model. For instance, linear regression is a distinct statistical method focused on fitting a linear equation to data, which does not apply to the branching structure of a Decision Tree. Additionally, Decision Trees do not rely on complex mathematical equations; instead, they operate through simple rules, making them generally interpretable. Lastly, while Decision Trees can handle both categorical and continuous output variables, they are not limited to continuous outputs only, which further distinguishes them from some other model types that are constrained in this manner.