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Coursera machine learning week 3 Quiz answer Regularization | Andrew Ng
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.
2. Suppose you ran logistic regression twice, once with 
, and once with 
. One of the times, you got parameters , and the other time you got
. However, you forgot which value of
corresponds to which value of 
. Which one do you think corresponds to ?
-
When
is set to 1, We use regularization to penalize large value of . Thus, the parameter, , obtained will in general have smaller values. -
-
2. Suppose you ran logistic regression twice, once with , and once with . One of the times, you got parameters , and the other time you got
. However, you forgot which value of
corresponds to which value of . Which one do you think corresponds to ?
-
-
When
is set to 1, We use regularization to penalize large value of . Thus, the parameter, , obtained will in general have smaller values.
-
3. Which of the following statements about regularization are true? Check all that apply.
- Using a very large value of hurt the performance of your hypothesis; the only reason we do not set to be too large is to avoid numerical problems.
- Because logistic regression outputs values
, its range of output values can only be “shrunk” slightly by regularization anyway, so regularization is generally not helpful for it. - Consider a classification problem. Adding regularization may cause your classifier to incorrectly classify some training examples (which it had correctly classified when not using regularization, i.e. when λ = 0).
- Using too large a value of λ can cause your hypothesis to overfit the data; this can be avoided by reducing λ.
- Using a very large value of
3. Which of the following statements about regularization are true? Check all that apply.
- Using a very large value of hurt the performance of your hypothesis; the only reason we do not set to be too large is to avoid numerical problems.
- Because logistic regression outputs values , its range of output values can only be “shrunk” slightly by regularization anyway, so regularization is generally not helpful for it.
- Because regularization causes J(θ) to no longer be convex, gradient descent may
not always converge to the global minimum (when λ > 0, and when using an
appropriate learning rate α). - Using too large a value of λ can cause your hypothesis to underfit the data; this can be avoided by reducing λ.
- Using a very large value of
4. In which one of the following figures do you think the hypothesis has overfit the training set?
- Figure:
- Figure:
- Figure:
- Figure:
- Figure:
5. 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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FAQs
Is Andrew Ng’s Machine Learning course good?
It is the Best Course for Supervised Machine Learning! Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!
How do I get answers to coursera assignment?
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How long does it take to finish coursera Machine Learning?
this specialization requires approximately 3 months with 75 hours of materials to complete, and I finished it in 3 weeks and spent an additional 1 week reviewing the whole course.
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Submit a programming assignment Open the assignment page for the assignment you want to submit. Read the assignment instructions and download any starter files. Finish the coding tasks in your local coding environment. Check the starter files and instructions when you need to. Reference
