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Amazon Exam MLS-C01 Topic 4 Question 80 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 80
Topic #: 4
[All MLS-C01 Questions]

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?

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Suggested Answer: A

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

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Chaya
22 days ago
Stemming and stop word removal? Sounds like my high school English teacher's dream tool. Maybe we can throw in some thesaurus action too, just for fun.
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Vallie
29 days ago
B) Amazon SageMaker BlazingText? Sounds like a made-up answer. I'd stick to the more well-known NLP tools and techniques.
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Novella
16 days ago
C: Natural Language Toolkit (NLTK) stemming and stop word removal could also be a good option to try.
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Shelia
17 days ago
B: Amazon SageMaker BlazingText? Sounds like a made-up answer. I'd stick to the more well-known NLP tools and techniques.
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Josphine
20 days ago
A: I think using Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers could help improve the validation accuracy.
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Sharika
1 months ago
A) Amazon Comprehend is probably overkill for this task. It's more suited for enterprise-level NLP tasks, not a simple sentiment analysis problem.
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Virgie
16 days ago
A) Amazon Comprehend is probably overkill for this task.
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Nicolette
17 days ago
D) Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
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Tyra
19 days ago
C) Natural Language Toolkit (NLTK) stemming and stop word removal
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Domitila
1 months ago
D) Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers could also be a good choice. TF-IDF can help identify the most important words in the dataset and reduce the impact of common words.
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Son
2 days ago
C: D) Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers could also be a good choice.
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Coral
1 months ago
B: I agree, removing stop words can help focus on the more important words in the dataset.
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Joanne
1 months ago
A: I think we should use C) Natural Language Toolkit (NLTK) stemming and stop word removal to improve the validation accuracy.
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Jamal
2 months ago
I prefer using NLTK for stemming and stop word removal to improve accuracy.
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Chauncey
2 months 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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Gianna
12 days ago
I agree, it can definitely help in handling the rich vocabulary and low word frequency.
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Delisa
19 days ago
I think using NLTK stemming and stop word removal could really help improve the accuracy.
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Kris
2 months ago
I agree with Florinda, TF-IDF can help with the low frequency of words issue.
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Florinda
2 months ago
I think we should use Scikit-learn TF-IDF vectorizers.
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