Coursera: Machine Learning (Week 3) Quiz - Regularization | Andrew NG

 

 

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2.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.

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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 ?
   •  .ANSWER
   •  

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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 ?
   •  
   •   ANSWER

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.

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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.

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