Machine Learning with Swift

Ridge regression and Tikhonov regularization

Under the standard least squares method, the obtained regression coefficients can vary wildly. We can formulate the least squares regression as an optimization problem:

What we have on the right here is just an RSS in a form of a scalar product. Tikhonov regularized least squares regression adds an additional penalty term—squared L₂ norm of weights vector:

where L₂ norm and λ is a scalar shrinkage ...