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Amazon Exam MLS-C01 Topic 3 Question 103 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 103
Topic #: 3
[All MLS-C01 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 dat

a. 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.)

Show Suggested Answer Hide Answer
Suggested Answer: A, E

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]


Contribute your Thoughts:

Rachael
5 days ago
This is a tough one, but I'm thinking the PCA route is the way to go. Reduce those features, baby! And who knows, maybe we'll stumble upon some hidden gems in the data. It's like a treasure hunt, but with smartphones instead of gold!
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Blair
8 days ago
Hold up, are we sure we can't just set the predictor type to regressor and call it a day? That sounds like the easy way out, but hey, sometimes the simplest solutions are the best, am I right?
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Marya
12 days ago
I reckon the k-NN algorithm is the way to go. Gotta love those nearest neighbors, they always have your back! Plus, with dimension reduction, we can really streamline the model and get it humming along nicely.
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Veronika
22 days ago
I'm not sure about that. Maybe trying option E with k-nearest neighbors could also be beneficial.
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Laquanda
27 days ago
I agree with Paulina. Reducing the number of features with PCA can make the model more accurate.
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Salena
1 months ago
Hmm, I'd say reducing the number of features with PCA is the way to go. That should help the model focus on the most important variables and improve the F1 score. And who knows, maybe we'll uncover some hidden insights in the process!
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Andra
10 days ago
A: Maybe we'll discover some interesting patterns by reducing the number of features with PCA.
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Kallie
11 days ago
B: Yeah, I agree. It's important to focus on the most relevant features for better accuracy.
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Afton
16 days ago
A: A sounds like a good idea. It could help simplify the model and improve performance.
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Paulina
1 months ago
I think option A could help improve the F1 score.
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