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

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

Question #: 70
Topic #: 5

[All Databricks-Certified-Professional-Data-Scientist 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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Dong

17 days ago

Definitely B. If you don't have enough data for a test set, N-fold cross-validation is your best friend. Can't imagine trying to evaluate a model without it in that case.

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Viola

18 days ago

Hah, N-fold cross-validation? More like N-fold headache, am I right? But seriously, it's the best way to handle that small data problem. Gotta do what you gotta do.

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Aaron

19 days ago

I'm not sure about the other options, but I know N-fold cross-validation is the way to go when you don't have enough data for a separate test set. Gotta make the most of what you've got!

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Tyisha

21 days ago

Wait, isn't N-fold cross-validation used to address overfitting when you have a small dataset? I'm pretty sure that's the right answer here.

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Dierdre

3 days ago

I think you're right, N-fold cross-validation helps prevent overfitting with small datasets.

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Mari

1 months ago

I think if there's not enough data to create a test set, we'd need to use N-fold cross-validation. That's the only way to properly evaluate the model's performance with limited data.

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