Society of Actuaries (SOA) PA Practice Exam 2026 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

Balancing the class distribution through techniques such as undersampling or oversampling leads to training the model on a more diverse dataset. This diversity is crucial because it helps ensure that the model learns from a more representative sample of both the majority and minority classes. When you balance the classes, you mitigate biases that might exist due to an uneven distribution, which often leads to improved generalization when the model is applied to new, unseen data.

In undersampling, you reduce the number of instances in the majority class, thereby giving the minority class a better chance to be represented more fully in the training data. In contrast, oversampling involves duplicating instances in the minority class or generating synthetic examples. Both techniques aim to create a dataset where minority classes are more prominently represented, leading to a more balanced and diverse dataset that enhances the model's ability to learn the characteristics of all classes equally well.

Ultimately, this approach contributes to better predictive performance, particularly for the minority class, which often suffers when class imbalance is present. Thus, it allows the model to make accurate predictions across different classes, demonstrating why training on a more diverse dataset is a key outcome of these balancing techniques.