L2regularizáció

L2 regularization, also known as ridge regression, is a technique used in machine learning and statistics to prevent overfitting by adding a penalty to the loss function. This penalty is proportional to the square of the magnitude of the coefficients, which encourages the model to keep the coefficients small. The regularization term is typically added to the ordinary least squares loss function, resulting in a modified loss function that the model aims to minimize.

The L2 regularization term is defined as the sum of the squares of the coefficients, multiplied by a regularization parameter (lambda). The regularization parameter controls the strength of the penalty, with larger values of lambda resulting in stronger regularization and smaller coefficients. The optimal value of lambda is often determined through cross-validation.

L2 regularization is particularly useful when dealing with multicollinearity, which occurs when predictor variables in a regression model are highly correlated. In such cases, L2 regularization can help to stabilize the estimates of the coefficients and improve the model's performance. Additionally, L2 regularization can be used in conjunction with other techniques, such as L1 regularization (lasso), to create elastic net regularization, which combines the benefits of both methods.

In summary, L2 regularization is a powerful tool for improving the performance and generalization of machine learning models by adding a penalty to the loss function that encourages small coefficients. It is widely used in various applications, including linear regression, logistic regression, and support vector machines.