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

Oversampling is a technique specifically designed to address the issue of unbalanced data. In many datasets, especially within fields like machine learning and actuarial science, instances of one class may significantly outnumber the others. This imbalance can lead to biased models that perform poorly on the underrepresented classes.

By employing oversampling, additional copies of the minority class instances are created to balance the dataset. This helps improve the model's ability to learn from the minority class without losing important information from the majority class. It effectively increases the representation of the minority class within the data, enabling the model to better understand and predict outcomes related to that class.

Normalization, while useful for scaling data to a specific range, does not specifically address class imbalance. Similarly, standardization focuses on transforming data so that it has a mean of zero and a standard deviation of one, but doesn't directly affect the distribution of class labels. Encapsulation, in a broader sense related to programming or data structures, does not pertain to data balancing techniques within datasets.

Thus, the choice of oversampling is appropriate as it directly targets the challenge of unbalanced datasets by adjusting the class distribution to facilitate more effective modeling.