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Question 250 - MLS-C01 discussion

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A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.

Which solution will meet this requirement with the LEAST development effort?

A.
Use SageMaker Data Wrangler to perform a Gini importance score analysis.
Answers
A.
Use SageMaker Data Wrangler to perform a Gini importance score analysis.
B.
Use a SageMaker notebook instance to perform principal component analysis (PCA).
Answers
B.
Use a SageMaker notebook instance to perform principal component analysis (PCA).
C.
Use a SageMaker notebook instance to perform a singular value decomposition analysis.
Answers
C.
Use a SageMaker notebook instance to perform a singular value decomposition analysis.
D.
Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
Answers
D.
Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
Suggested answer: A

Explanation:

SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.

References:

* Analyze and Visualize

* Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

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asked 16/09/2024
Jose M Rivera Vega
38 questions
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