Ridge Regression (also called Tikhonov regularization) is a regularized version of Linear Regression: a regularization term equal to is added to the cost function. This forces the learning algorithm to not only fit the data but also keep the model weights as small as possible. Note that the regularization term should only be added to the cost function during training. Once the model is trained, you want to evaluate the model’s performance using the unregularized performance measure.
