Society of Actuaries (SOA) PA Practice Exam

The correct answer highlights the utility of both undersampling and oversampling in addressing class imbalance in datasets. When dealing with skewed distributions of data — where one class, often the minority class, is significantly less represented than the others — these techniques help to balance the representation of each class. Oversampling involves increasing the number of instances in the minority class, often by duplicating existing instances or creating synthetic data points. This approach provides the model with more information about the minority class, helping it to better learn its characteristics and improve prediction accuracy for that class. On the other hand, undersampling reduces the number of instances in the majority class to better balance the dataset. This can help to prevent the model from being biased toward the majority class and improves the model's ability to recognize patterns from the minority class. Both methods aim to create a more balanced dataset that enables the predictive model to perform optimally across all classes, enhancing overall predictive accuracy and ensuring the model does not disproportionately favor the majority class. As a result, the prediction performance improves, especially for the minority class, which is crucial in many applications, such as fraud detection or disease diagnosis, where identifying rare events is vital. These methods can lead to improved predictive performance, particularly in scenarios where class imbalance