Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

Regularization methods such as Lasso and Ridge Regression primarily aim to prevent overfitting in predictive modeling. Overfitting occurs when a model learns not only the underlying pattern in the training data but also the noise, leading to poor performance on unseen data. Regularization introduces a penalty for larger coefficients in the model, which effectively reduces the complexity of the model by discouraging reliance on any one feature excessively.

By applying these penalties, Lasso and Ridge Regression help to maintain a balance between fitting the training data well and keeping the model general enough to perform effectively on new, unseen data. This balance leads to improved predictive performance, especially in situations where the number of predictors is large compared to the number of observations, or when the predictors are highly correlated.

While increasing accuracy can be a consequence of using these methods, it is not the primary goal; rather, the focus is squarely on creating a more robust model that generalizes better. The other choices do not align with the fundamental intent of regularization techniques. For instance, increasing model complexity runs counter to the purpose of regularization, as does aiming solely for a reduction in computational time without consideration of model performance.