L2 regularization is a popular technique used in machine learning to prevent overfitting of the data by adding a penalty term to the loss function. It is also known as Ridge Regression and is one of the most effective regularization techniques.

Overfitting occurs when a model captures noise or random fluctuations in the data instead of the underlying relationships between the features and the target variable. This results in the model performing well on the training data but poorly on new, unseen data. Regularization techniques are used to avoid overfitting by adding a penalty term to the loss function which reduces the magnitude of the weights that are responsible for the overfitting.

The L2 regularization technique adds a penalty term to the loss function that is proportional to the square of the L2 norm of the weight vector. The L2 norm is the square root of the sum of the squared values of the weight vector. The penalty term is usually controlled by a parameter λ, which is known as the regularization strength. A higher value of λ results in a larger penalty term which leads to smaller weights and reduces overfitting.

The L2 regularization technique is particularly useful in regression tasks where the number of features is large compared to the number of samples. In such cases, the model tends to overfit the data and the L2 regularization technique helps in reducing the variance of the model by shrinking the weights towards zero. This results in a simpler model that generalizes better on new data.

In addition to reducing overfitting, the L2 regularization technique has other benefits as well. It helps in improving the conditioning of the optimization problem by reducing the sensitivity of the weights to small changes in the features. It also helps in improving the interpretability of the model by reducing the influence of irrelevant features.

In summary, the L2 regularization technique is a powerful tool for preventing overfitting of the data and improving the robustness of the model. It is a widely used technique in machine learning and is particularly useful in regression tasks with high dimensional data. By adding a penalty term to the loss function, the L2 regularization technique helps in reducing the variance of the model and improving its generalization performance on new data.