L2szabályozás
L2szabályozás, also known as L2 regularization, is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty is proportional to the sum of the squares of the model's weights. The goal of L2 regularization is to shrink the weights towards zero, but not necessarily to exactly zero. This encourages the model to use smaller weights, which generally leads to a simpler and more robust model that is less sensitive to individual data points.
The mathematical formulation of L2 regularization involves adding a term to the standard loss function. If
L2 regularization is widely used in various machine learning algorithms, including linear regression (often referred to