Hands-On Machine Learning with C++

L2 regularization – Ridge

L2 regularization is also an additional term to the loss function:

This additional term adds a squared value of magnitude of parameters as a penalty. λ is also a coefficient of regularization. Its higher values lead to stronger regularization and can lead to underfitting. Another name for this regularization type is Ridge regularization. Unlike L1 regularization, this type does not have a feature selection characteristic. Instead, we can interpret it as a model smoothness configurator. In addition, L2 regularization is computationally more efficient for gradient descent-based optimizers because its differentiation ...