# ▸ Regularization :

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1. You are training a classification model with logistic regression. Which of the following statements are true? Check all that apply.
• Introducing regularization to the model always results in equal or better performance on the training set.
• Introducing regularization to the model always results in equal or better performance on examples not in the training set.
• Adding a new feature to the model always results in equal or better performance on the training set.
• Adding many new features to the model helps prevent overfitting on the training set.

1. Suppose you ran logistic regression twice, once with $\inline&space;\lambda&space;=&space;0$, and once with $\inline&space;\lambda&space;=&space;1$. One of the times, you got parameters $\inline&space;\theta&space;=&space;\begin{bmatrix}&space;74.81\\&space;45.05&space;\end{bmatrix}$, and the other time you got $\inline&space;\theta&space;=&space;\begin{bmatrix}&space;1.37\\&space;0.51&space;\end{bmatrix}$. However, you forgot which value of $\inline&space;\lambda$ corresponds to which value of $\inline&space;\theta$. Which one do you think corresponds to $\inline&space;\lambda&space;=&space;1$?

1. Suppose you ran logistic regression twice, once with $\inline&space;\lambda&space;=&space;0$, and once with $\inline&space;\lambda&space;=&space;1$. One of the times, you got parameters $\inline&space;\theta&space;=&space;\begin{bmatrix}&space;81.47\\&space;12.69&space;\end{bmatrix}$, and the other time you got $\inline&space;\theta&space;=&space;\begin{bmatrix}&space;13.01\\&space;0.91&space;\end{bmatrix}$. However, you forgot which value of $\inline&space;\lambda$ corresponds to which value of $\inline&space;\theta$. Which one do you think corresponds to $\inline&space;\lambda&space;=&space;1$?

1. Which of the following statements about regularization are true? Check all that apply.

1. Which of the following statements about regularization are true? Check all that apply.

1. In which one of the following figures do you think the hypothesis has overfit the training set?
• Figure:
• Figure:
• Figure:
• Figure:

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1. In which one of the following figures do you think the hypothesis has underfit the training set?
• Figure:
• Figure:
• Figure:
• Figure:

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1. Why 1. cannot be D)? I could not see any difference between example and training set.

1. I mean alternative B (instead of D)

3. Adding many new features will overfit the data instead of preventing overfitting

4. @Unknown True. Agreed.
That's the reason Why D is the wrong Answer for Q1.

5. Hi,
Yeah you are right, Adding many new features can cause overfitting and regularization such as L1 and L2 are actually pushing effects of some features close to zero. I am not sure why its not considered as correct answer in this quiz