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

Regularization regression is a technique used to prevent overfitting of a model by adding a penalty to the size of the coefficients. The goal is to shrink the coefficient estimates toward zero, which helps maintain significant predictive power while reducing the impact of collinearity and noise in the data. This shrinkage encourages the model to be simpler and more generalizable to new data.

By reducing the values of the coefficients, regularization helps to avoid complex models that can fit the training data too closely, which is often a source of poor performance on unseen data. This approach allows the model to retain the most important features while diminishing the influence of less important or redundant predictors. Therefore, the primary effect of regularization is to push those coefficient estimates closer to zero, leading to a more robust and interpretable model.

This technique contrasts with increasing, randomly adjusting, or completely eliminating coefficient estimates, as those approaches do not directly address the issue of overfitting or improving model generalization in the same way that shrinkage does.