Regularization is a powerful technique used in machine learning to prevent overfitting, ensuring that models generalize well to unseen data. In this comprehensive guide, we will discuss the various forms of regularization, their significance, and how they can improve model performance. If you're looking to enhance your machine learning skills, understanding regularization is essential.

What is Regularization?

Regularization introduces a penalty term to the optimization problem that the algorithm solves during training. This penalty discourages the model from fitting the noise in the training data, thereby enhancing the model's ability to generalize.