Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Regularization is a crucial technique used in machine learning that serves the primary purpose of preventing overfitting. When a model is trained on a dataset, it aims to learn the underlying patterns. However, if the model becomes too complex, it can start to fit the noise present in the training data rather than just the actual signal. This leads to a model that performs well on the training data but poorly on unseen data, resulting in overfitting.

By adding penalties to model complexity through regularization techniques such as L1 (Lasso) or L2 (Ridge) regularization, we encourage the model to keep its weights small and to focus on the most important features rather than all available data. This balance helps improve the model's generalization to new, unseen data, ultimately leading to better performance in practice.

Thus, the core objective of regularization is to mitigate overfitting by controlling the capacity of the model and ensuring that it does not become excessively complex.