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

Using information gain as a criterion in decision tree training primarily focuses on how well it helps in classifying data correctly by evaluating the effectiveness of features in splitting the data into different classes. The concept of information gain measures the reduction in entropy or uncertainty about the target variable when the dataset is split based on a specific feature. When training a decision tree, choosing splits that maximize information gain leads to decisions that group similar instances together, thereby enhancing the model's ability to classify unseen data accurately. This is why the option concerning improved classification accuracy is the key outcome related to information gain.

In the context of decision trees, while choosing splits can inadvertently alter the decision boundary, the principal aim of using information gain is to increase the likelihood of correctly classifying data, rather than intentionally modifying these boundaries. Furthermore, while decision trees aim for optimal splits, information gain does not guarantee that all possible splits will be achieved—rather, it seeks the best splits based on the current evaluations. Lastly, reducing data noise is not directly addressed by information gain; instead, it focuses on improving the clarity of the decision-making process and classification.