What does L2 regularization add to the loss function during training?

The correct answer emphasizes that L2 regularization adds the sum of the squared parameter weights to the loss function during training. This approach is a common technique used to prevent overfitting in machine learning models. By incorporating the squared weights, L2 regularization effectively penalizes large weights in the model, encouraging the model to maintain smaller weight values. This regularization term is mathematically represented as ( \lambda \sum w_i^2 ), where ( \lambda ) is the regularization strength and ( w_i ) are the model parameters.

The inclusion of this term in the loss function modifies the optimization process, as the model not only seeks to minimize the primary loss associated with the training data but also aims to keep the weights of the model relatively small. This results in a smoother and more generalized model that can perform better on unseen data.

Understanding this concept helps illustrate why L2 regularization can enhance a model’s performance, not just by reducing its complexity but also by enabling it to maintain a better balance between fitting the training data and generalizing to new observations.