Society of Actuaries (SOA) PA Practice Exam 2025 - Free Actuarial Practice Questions and Study Guide

The training process of Decision Trees is best described by the way they infer decision rules from data features in a tree-like structure. In this method, data is split into subsets based on the value of the input features, creating branches that lead to decision nodes and ultimately to leaves that represent the output or classification. This structure allows the model to capture complex relationships and interactions between features without making assumptions about the form of the relationship.

In contrast, other options either suggest limitations that do not apply to Decision Trees or misrepresent their methodology. For instance, the idea that a fixed step size is required for evaluations pertains more to gradient descent optimization techniques, which is not relevant for Decision Trees. Additionally, the notion of processing all variables simultaneously may imply that all features need to be considered at once, which does not align with how Decision Trees work; they evaluate one feature at a time to make splits in the data. Lastly, the focus on linear relationships does not apply to Decision Trees, as they are explicitly designed to model both linear and non-linear interactions among features.