- Regularization can avoid underfitting/overfitting. But how it does acttually affect the learning algorithms

- Remember the regularization indexes from 1
- Set lambda Â = 1000, and each parameters will be highly penalized and will be tend to flat graph, resulting to underfitting
- In contrast, set lambda to 0, the parameters will not be penalized and resulting in overfitting problems
- So how we choose the correct value of regularization (lambda)?

- Using extra lambda, just using average of the training set
- Jtrain, Jcv,Jtest in earlier without the regularization

- Try variant range of lambda by multiple sets of two
- Iterate each model of theta use it to cost function
- Use the theta into cross validation set
- Pick whichever model that has the lowest value in cross validation error
- And compare it to Jtest error
- Concretely, use the selection model of thetas with selection of lambda,(model 5 with lambda no.5), and pick whichever has the lowest error of Jcv
- That's the summary of model selection for regularization