Ridge regression
[Regularized parameter](https://texonom.com/regularized-parameter-f3b208cdd37a4002a5d39ef990b8be33) [L2 Norm](https://texonom.com/l2-norm-38c15917350a4c82a11003474ac7d280) 


### bias 더하고, variance 줄이는 방법으로 regulation 수행
[Regularization](https://texonom.com/regularization-c85433c8c0554eba8edf0035b7fd334c) concept is first adapted to Ridge Regression model
not like linear regression, focus on Regularizer too not only on Fitting error
$$J(\theta) = \frac{1}{2}(\theta\Phi - \vec{y})^T(\theta\Phi - \vec{y}) + \frac{\lambda}{2}\theta^T\theta$$
find 0 point in function derivation by $\theta$
- $\lambda$ controls the tradeoff between Fitting error, Regularizer (overfitting and underfitting) 
**alpha, lamda, regularization parameter, penalty term is same**





> [Lasso, Ridge regularization - 회귀에서 selection과 shrinkage](https://sosal.kr/1104)

> [Ridge regression (릿지 회귀) python](https://signature95.tistory.com/46)

> [Ridge regression(릿지회귀)](https://velog.io/@zhenxi_23/Ridge-regression릿지회귀)





