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Dell EMC D-DS-FN-23 Exam - 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?

AHandles correlated variables and handles missing variables

BHandles categorical variables and handles numeric variables

CWell calibrated and easy to implement

DHandles very high dimensional data and resistant to over-fitting

Show Suggested Answer

Suggested Answer: D

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

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Oneida

7 months ago

It's also good for high dimensional data, right?

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Margret

7 months ago

Totally agree, it's super easy to implement too.

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Dierdre

7 months ago

Wait, does it really handle correlated variables well? I thought it struggled with that.

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Brock

8 months ago

Yeah, and it's pretty resistant to overfitting!

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Coletta

8 months ago

Naive Bayes is great for categorical and numeric data!

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Paola

8 months ago

I feel like Naive Bayes is particularly useful for high-dimensional data, but I’m not clear on how it deals with missing values.

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Lazaro

8 months ago

I practiced a question about Naive Bayes and I think it mentioned something about being resistant to overfitting, but I’m not entirely confident.

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Apolonia

8 months ago

I think one of the reasons to use Naive Bayes is its simplicity and ease of implementation, but I can't recall if it's well calibrated.

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Grover

9 months ago

I remember that Naive Bayes is great for handling categorical data, but I'm not sure about its performance with numeric variables.

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Tyra

9 months ago

I'm a bit confused by this question. The options seem to cover a range of different capabilities, and I'm not sure which ones are actually advantages of the naive Bayes classifier. I'll have to review my notes on this algorithm before answering.

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Ashlee

9 months ago

Okay, let me think this through. I know naive Bayes makes some strong independence assumptions, so options A and D don't sound quite right. I think C is the best answer here - it's well-calibrated and easy to implement.

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Phung

9 months ago

Hmm, I'm a bit unsure about this one. I know naive Bayes is a simple and fast algorithm, but I'm not sure about the specific advantages listed in the options.

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Catarina

9 months ago

This looks like a straightforward question on the advantages of a naive Bayes classifier. I should be able to handle this.

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Annita

1 year 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 year 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 year 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

1 year ago

C is definitely a good reason too. It's well calibrated and easy to implement.

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Earnestine

1 year 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

1 year ago

I agree, Naive Bayes is a great choice for handling categorical variables. B)

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Ellsworth

1 year 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 year 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

1 year ago

Definitely, handling very high dimensional data is a big advantage.

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Afton

1 year ago

I agree, D is a great reason to develop a Naive Bayes classifier model.

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Clarence

1 year 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 year ago

User 3: I agree, D seems like the best option for handling complex data.

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Glennis

1 year ago

User 2: Yeah, D is definitely a good choice. It's resistant to over-fitting too.

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Lucia

1 year ago

User 1: D sounds perfect for your project. It handles high-dimensional data well.

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Filiberto

1 year 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

1 year 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

1 year ago

D) Handles very high dimensional data and resistant to over-fitting

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Genevieve

1 year ago

I agree, it's a tough choice between the two.

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Agustin

1 year ago

C) Well calibrated and easy to implement

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Irving

1 year ago

B) Handles categorical variables and handles numeric variables

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Angelica

1 year ago

Option C looks good to me. Easy to implement and well-calibrated? Sign me up!

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Na

1 year ago

Definitely! Having a well-calibrated model can make a big difference in the accuracy of your predictions.

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Alaine

1 year 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

1 year ago

Option C looks good to me. Easy to implement and well-calibrated? Sign me up!

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