Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 5 Question 70 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist Exam exam

Question #: 70
Topic #: 5

[All Databricks Certified Professional Data Scientist Exam Questions]

Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

AThe data is unformatted.

BThere is not enough data to create a test set.

CThere are missing values in the data.

DThere are categorical variables in the model.

Show Suggested Answer

Suggested Answer: B

by Amina at Sep 13, 2024, 12:55 PM

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Felix

5 days ago

If the data is unformatted, I'd say you need a crystal ball and a unicorn to solve this problem. Cross-validation ain't gonna cut it, my friend.

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Elbert

7 days ago

C is the way to go. Missing values? Time for some good old-fashioned cross-validation to the rescue!

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Mollie

9 days ago

I believe we should also consider using cross-validation if there is not enough data to create a test set.

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Marg

11 days ago

I agree with Darrin. Cross-validation helps in estimating the model's performance when there are missing values.

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Darrin

12 days ago

I think we need to implement N-fold cross-validation when there are missing values in the data.

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Billye

12 days ago

Hmm, I'm not sure. Wouldn't you want to do cross-validation regardless of the data issues? Better safe than overfitting, am I right?

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Yuriko

21 days ago

D makes the most sense to me. Handling categorical variables is tricky, and cross-validation can help ensure the model generalizes well.

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Dominga

25 days ago

I'd go with B. If there's not enough data for a test set, cross-validation is a great way to get a reliable performance estimate.

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Rosita

11 days ago

B) There is not enough data to create a test set.

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