Description: L2 regularization, also known as Tikhonov regularization, is a technique used in machine learning and neural network training to prevent overfitting. This technique adds a penalty to the loss function that is proportional to the square of the magnitude of the model’s coefficients. Mathematically, a term of the form λ * ||w||² is added to the loss function, where λ is a hyperparameter that controls the strength of the regularization and ||w||² is the L2 norm of the model’s weights. This penalty encourages models to keep weights small, which in turn helps improve the model’s generalization to unseen data. L2 regularization is particularly useful in contexts with a large number of features or when the data is noisy, as it helps stabilize parameter estimates. Additionally, it has become a common practice in hyperparameter optimization and in training various machine learning models, including deep learning architectures, where model complexity can lead to significant overfitting. In summary, L2 regularization is an essential tool in the machine learning arsenal, contributing to the robustness and effectiveness of models.
History: L2 regularization has its roots in statistical estimation theory and was formalized in the context of machine learning in the 1990s. Its development is associated with the work of several researchers in the field of statistics and statistical learning, who sought methods to improve model generalization. As machine learning gained popularity, L2 regularization became a standard technique for addressing overfitting in complex models.
Uses: L2 regularization is widely used in various machine learning applications, including linear and logistic regression, neural networks, and deep learning models. It is particularly effective in situations with a large number of features or when the data is noisy, helping to improve the stability and generalization of models.
Examples: A practical example of L2 regularization can be seen in the implementation of machine learning frameworks, where an L2 regularization term can be easily added to the loss function. Another case is in linear regression, where L2 regularization is used to prevent coefficients from fitting too closely to the training data, which could result in a model that does not generalize well to new data.
