Why Regularization Is Good?

Regularization, significantly reduces the variance of the model, without substantial increase in its bias. ... As the value of λ rises, it reduces the value of coefficients and thus reducing the variance.

What is regularization and why is it useful?

Regularization is a technique used to reduce the errors by fitting the function appropriately on the given training set and avoid overfitting. The commonly used regularization techniques are : L1 regularization. L2 regularization.

Is regularization always good?

Regularization does NOT improve the performance on the data set that the algorithm used to learn the model parameters (feature weights). However, it can improve the generalization performance, i.e., the performance on new, unseen data, which is exactly what we want.