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Exam AWS Certified Machine Learning - Specialty topic 1 question 199 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 199
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2,000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.

Which changes in model training would MOST likely improve the model's F1 score? (Choose two.)

  • A. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
  • B. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-learn multi-dimensional scaling (MDS) algorithm.
  • C. Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
  • D. Use the SageMaker k-means algorithm with k of less than 1,000 to train the model.
  • E. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.
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Suggested Answer: AE 🗳️
by dunhill at Nov. 28, 2022, 6:30 p.m.
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Peeking
Highly Voted 1 year, 4 months ago
Selected Answer: AE
KNN can be used for dimensionality reduction through NCA (https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#)
upvoted 10 times
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Jerry84
Highly Voted 1 year, 3 months ago
Selected Answer: AE
A. Correct B. Incorrect. MDS is Non-linear dimensionality reduction method. https://towardsdatascience.com/11-dimensionality-reduction-techniques-you-should-know-in-2021-dcb9500d388b C. Incorrect. This is a classification problem instead of Regression. D. Incorrect. K-means is for Clustering(Unsupervised learning). E. Correct.
upvoted 7 times
kaike_reis
8 months, 2 weeks ago
Why "Non-linear dimensionality reduction method" is a problem? We can add non linear features as x^2 to a model to improve performance.
upvoted 2 times
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drcok87
1 year, 2 months ago
E : https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html
upvoted 2 times
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Pt8442
Most Recent 6 months, 2 weeks ago
B is wrong as Multidimensional Scaling sits under the Unsupervised branch of Machine Learning algorithms
upvoted 1 times
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loict
7 months, 3 weeks ago
Selected Answer: AE
A. YES - F1 score is low. Reducing feature count could improve F1 score. B. NO - MDS is for visualization C. NO - regressor is to predict a numerical value, we want classification D. NO - K-means is clustering, we want classification E. YES - k-Means could work if a linear model is not best
upvoted 1 times
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Mickey321
8 months ago
Selected Answer: AE
A & E are correct
upvoted 1 times
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kaike_reis
8 months, 2 weeks ago
Selected Answer: AB
A and B are correct. But I understand E being correct as well.
upvoted 1 times
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Mllb
1 year ago
Selected Answer: AE
https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html
upvoted 6 times
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dunhill
1 year, 5 months ago
I think the answer is AB. k-means and k-nearest neighbors are not for reduce dimension.
upvoted 2 times
cox1960
12 months ago
k-nn does. https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html
upvoted 2 times
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