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Question 274 - MLS-C01 discussion

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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.

What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

A.
Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
Answers
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-iearn multi-dimensional scaling (MDS) algorithm.
Answers
B.
Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-iearn multi-dimensional scaling (MDS) algorithm.
C.
Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
Answers
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
Answers
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.
Answers
E.
Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.
Suggested answer: A, E

Explanation:

Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model's performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.

Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model's performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.

Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.

Option C is incorrect because setting the predictor type to regressor would change the model's objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.

Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.

References:

Amazon SageMaker Linear Learner Algorithm

Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm

[Principal Component Analysis - Scikit-learn]

[Multidimensional Scaling - Scikit-learn]

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asked 16/09/2024
Austine Ogheneruemu Onakpoma
42 questions
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