Society of Actuaries (SOA) PA Practice Exam 2026 - Free Actuarial Practice Questions and Study Guide

Oversampling is primarily employed to address the challenges posed by unbalanced datasets, where one class (often the minority) is significantly underrepresented compared to another class (the majority). The principal goal of oversampling is to increase the number of instances in the minority class. By doing so, it helps to create a more balanced dataset that allows models to learn effectively and reduces the risk of biased predictions toward the majority class.

This technique involves duplicating existing instances of the minority class or generating synthetic examples, which can enhance the learning process by providing more data points for that class. With a more balanced dataset, classifiers can perform better, as they have the opportunity to learn diverse patterns from both classes.

While ensuring equal representation of all classes or decreasing instances of the majority class might align with some objectives in certain contexts, the core aim of oversampling is to bolster the minority class instances specifically, thereby enhancing the model's ability to generalize and make accurate predictions across all classes.