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Question 283 - MLS-C01 discussion

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A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question.

A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model.

Which solution will meet these requirements?

A.

Perform one-hot encoding on every possible option for each question of the survey.

Answers
A.

Perform one-hot encoding on every possible option for each question of the survey.

B.

Perform binning on all the answers each respondent selected for each question.

Answers
B.

Perform binning on all the answers each respondent selected for each question.

C.

Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.

Answers
C.

Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.

D.

Use Amazon Textract to create numeric features for each set of possible responses.

Answers
D.

Use Amazon Textract to create numeric features for each set of possible responses.

Suggested answer: A

Explanation:

In cases where survey questions allow multiple choices per question, one-hot encoding is an effective way to represent responses as binary features. Each possible option for each question is transformed into a separate binary column (1 if selected, 0 if not), providing a comprehensive and machine-readable format that logistic regression models can interpret effectively.

This approach ensures that each respondent's selections are accurately captured in a format suitable for training, offering a straightforward representation for multi-choice responses.

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asked 31/10/2024
Albaladejo Joffrey
37 questions
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