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Exam AWS Certified Machine Learning - Specialty topic 1 question 233 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 233
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

An online advertising company is developing a linear model to predict the bid price of advertisements in real time with low-latency predictions. A data scientist has trained the linear model by using many features, but the model is overfitting the training dataset. The data scientist needs to prevent overfitting and must reduce the number of features.

Which solution will meet these requirements?

  • A. Retrain the model with L1 regularization applied.
  • B. Retrain the model with L2 regularization applied.
  • C. Retrain the model with dropout regularization applied.
  • D. Retrain the model by using more data.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️
by sevosevo at March 18, 2023, 2:06 p.m.
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loict
9 months, 3 weeks ago
Selected Answer: A
A. YES - best to reduce feature count B. NO - L2 will reduce large weights and smooth features, to get rid of them C. NO - dropout is for NN D. NO - we are already converging, no need for more data
upvoted 2 times
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Mickey321
10 months, 2 weeks ago
Selected Answer: A
L1 regulization
upvoted 2 times
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jackzhao
1 year, 3 months ago
A is correct
upvoted 3 times
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GiorgioGss
1 year, 3 months ago
Selected Answer: A
L1 shrinks the less important feature’s coefficient to zero thus, removing some feature altogether. So, this works well for feature selection in case we have a huge number of features.
upvoted 2 times
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blanco750
1 year, 3 months ago
Selected Answer: A
Yes L1 for feature reduction
upvoted 2 times
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sevosevo
1 year, 3 months ago
Selected Answer: A
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwim5YGd0-X9AhXPYcAKHSAlAkoQFnoECA0QAQ&url=https%3A%2F%2Fdocs.aws.amazon.com%2Fmachine-learning%2Flatest%2Fdg%2Fmodel-fit-underfitting-vs-overfitting.html&usg=AOvVaw2jwLt-J0jRSWeiDyjEzI_S
upvoted 3 times
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C (25%)
B (20%)
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