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Dell EMC Exam D-DS-FN-23 Topic 5 Question 1 Discussion

Actual exam question for Dell EMC's D-DS-FN-23 exam
Question #: 1
Topic #: 5
[All D-DS-FN-23 Questions]

What are good reasons to develop a nave Bayes classifier model?

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Suggested Answer: D

by Louvenia at May 01, 2025, 02:20 AM

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Annita
1 months ago
In addition, Naive Bayes is known for handling very high dimensional data and being resistant to over-fitting, which are important considerations in model development.
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Jesusa
1 months ago
I also think that Naive Bayes is well calibrated and easy to implement, which makes it a good choice for many applications.
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Francine
1 months ago
I heard Naive Bayes is the 'Goldilocks' of machine learning models - not too complex, not too simple, but just right. C is my pick!
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Franklyn
19 days ago
C is definitely a good reason too. It's well calibrated and easy to implement.
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Earnestine
21 days ago
I think D is also a good reason to develop a Naive Bayes classifier model. It can handle very high dimensional data.
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Aretha
30 days ago
I agree, Naive Bayes is a great choice for handling categorical variables. B)
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Ellsworth
1 months ago
I agree with Filiberto. It's important to have a model that can handle different types of variables.
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Krissy
1 months ago
As a data scientist, I can't resist the siren call of a model that's resistant to over-fitting. D gets my vote!
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Mirta
26 days ago
Definitely, handling very high dimensional data is a big advantage.
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Afton
28 days ago
I agree, D is a great reason to develop a Naive Bayes classifier model.
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Clarence
2 months ago
D sounds like the way to go. Handling high-dimensional data and avoiding over-fitting? That's exactly what I need for my project.
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Cherry
1 months ago
User 3: I agree, D seems like the best option for handling complex data.
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Glennis
1 months ago
User 2: Yeah, D is definitely a good choice. It's resistant to over-fitting too.
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Lucia
1 months ago
User 1: D sounds perfect for your project. It handles high-dimensional data well.
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Filiberto
2 months ago
I think a good reason to develop a Naive Bayes classifier model is because it handles categorical variables and numeric variables.
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Glen
2 months ago
I'm torn between B and C. Handling both categorical and numeric variables is a big plus, but the simplicity of C is hard to beat.
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Hollis
26 days ago
D) Handles very high dimensional data and resistant to over-fitting
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Genevieve
1 months ago
I agree, it's a tough choice between the two.
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Agustin
1 months ago
C) Well calibrated and easy to implement
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Irving
2 months ago
B) Handles categorical variables and handles numeric variables
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Angelica
3 months ago
Option C looks good to me. Easy to implement and well-calibrated? Sign me up!
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Na
1 months ago
Definitely! Having a well-calibrated model can make a big difference in the accuracy of your predictions.
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Alaine
2 months ago
Yes, it's a great choice. It's important to have a model that is easy to implement and gives accurate results.
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Rolland
2 months ago
Option C looks good to me. Easy to implement and well-calibrated? Sign me up!
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