Amazon Exam MLS-C01 Topic 4 Question 80 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 80
Topic #: 4

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset

Which tool should be used to improve the validation accuracy?

AAmazon Comprehend syntax analysts and entity detection

BAmazon SageMaker BlazingText allow mode

CNatural Language Toolkit (NLTK) stemming and stop word removal

DScikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Show Suggested Answer

Suggested Answer: A

by Veronika at Oct 02, 2023, 04:25 PM

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Jamal

5 days ago

I prefer using NLTK for stemming and stop word removal to improve accuracy.

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Chauncey

5 days ago

C) Natural Language Toolkit (NLTK) stemming and stop word removal seems like the best option to handle the issue of rich vocabulary and low average word frequency. Removing common words and reducing words to their base form can help improve the model's performance.

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Kris

8 days ago

I agree with Florinda, TF-IDF can help with the low frequency of words issue.

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Florinda

17 days ago

I think we should use Scikit-learn TF-IDF vectorizers.

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