Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

Regularization serves a crucial purpose in enhancing the generalization ability of machine learning models. Its primary function is to prevent overfitting, which occurs when a model learns to capture noise and details in the training data to such an extent that it negatively impacts its performance on unseen data. By introducing a penalty for model complexity within the loss function, regularization encourages the development of simpler models that are less likely to overfit.

In practice, regularization can take various forms, such as L1 (Lasso) and L2 (Ridge) regularization. These methods add a term to the loss function based on the magnitude of the model's coefficients. This penalty promotes scenarios where the model retains only the most relevant features, thus enhancing its ability to generalize to new data.

The other options do not accurately capture the primary role of regularization. Ensuring sufficient data for training pertains to data management rather than model complexity. Improving neural network architecture focuses on structural design aspects rather than controlling overfitting. Enhancing interpretability relates more to how results are presented and understood, which is a separate concern from managing the complexity of the model itself.