Perform regularization as a method to increase bias and reduce variance
Regularization concept is first adapted to Ridge Regression model
not like linear regression, focus on Regularizer too not only on Fitting error
Linear regression + L2 Norm
find 0 point in function derivation by
- controls the tradeoff between Fitting error, Regularizer (overfitting and underfitting)
alpha, lambda, regularization parameter, penalty term is same
Ridge regression shrinks the model’s coefficients but not does not make them zero.
