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Exam AWS Certified Machine Learning - Specialty topic 1 question 277 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 277
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

An exercise analytics company wants to predict running speeds for its customers by using a dataset that contains multiple health-related features for each customer. Some of the features originate from sensors that provide extremely noisy values.

The company is training a regression model by using the built-in Amazon SageMaker linear learner algorithm to predict the running speeds. While the company is training the model, a data scientist observes that the training loss decreases to almost zero, but validation loss increases.

Which technique should the data scientist use to optimally fit the model?

  • A. Add L1 regularization to the linear learner regression model.
  • B. Perform a principal component analysis (PCA) on the dataset. Use the linear learner regression model.
  • C. Perform feature engineering by including quadratic and cubic terms. Train the linear learner regression model.
  • D. Add L2 regularization to the linear learner regression model.
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Suggested Answer: A 🗳️
by Aaabbk at Dec. 14, 2023, 10:18 p.m.
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data_sma
4 months ago
Selected Answer: A
L1 so the weight for the noisy features can go to zero
upvoted 1 times
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LeoD
4 months, 1 week ago
Selected Answer: D
Although this document (https://docs.aws.amazon.com/machine-learning/latest/dg/training-parameters1.html#regularization) relate noise with L1 in its content, if you were to use L1 regularization, the algorithm might push too many weights to zero. This could inadvertently remove useful information, especially if the noisy features still contain some signal. So in this case it should be L2 instead of L1.
upvoted 2 times
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MultiCloudIronMan
7 months ago
Selected Answer: D
L2 regularization is generally more effective for reducing overfitting in the presence of noisy data because it reduces the impact of all features proportionally, rather than eliminating some features entirely as L1 regularization does.
upvoted 2 times
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AIWave
1 year, 1 month ago
Selected Answer: A
Both L1 & L2 help with overfitting. However, L1 regularization does feature selection by reducing weights of rerelenat featurtes to zero - reducing dimensionality and removing noisy features as in this case. L2 on the other hand keeps all features including noisy ones.
upvoted 2 times
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CloudHandsOn
1 year, 3 months ago
Selected Answer: A
If you suspect that some features are irrelevant, Option A (L1 Regularization) could be more effective as it can shrink some coefficients to zero, effectively performing feature selection be removing the noise. If you believe that most features are relevant but the model is too complex, Option D (L2 Regularization) is typically the better choice as it evenly shrinks all coefficients, thus reducing model complexity without eliminating features. In this case, option A would be ideal to get rid of the irrelevant noise.
upvoted 2 times
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xiaoeason
1 year, 4 months ago
Selected Answer: D
It should be D as this is a overfitting problem. A might make the model oversimple in that case train acc will be bad. L2 is better than L1
upvoted 4 times
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Ryan10000
1 year, 4 months ago
Selected Answer: A
A. L1 Regularization reduces the amount of noise in the model, https://docs.aws.amazon.com/machine-learning/latest/dg/training-parameters1.html
upvoted 4 times
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Aaabbk
1 year, 4 months ago
D L2 regularisation for overfitting and noise
upvoted 3 times
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