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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?

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Suggested Answer: B

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

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Dong
2 months 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
2 months 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
2 months 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
2 months 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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Elza
15 days ago
So, the correct answer would be both B) and C) then.
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Laticia
18 days ago
Yes, that's true. It can also be used when there are missing values in the data.
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Loreta
26 days ago
But isn't it also used when there are missing values in the data?
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Dierdre
1 months ago
I think you're right, N-fold cross-validation helps prevent overfitting with small datasets.
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Mari
2 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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Willetta
27 days ago
C) There are missing values in the data.
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Mendy
28 days ago
B) There is not enough data to create a test set.
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Jarod
1 months ago
A) The data is unformatted.
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Felix
2 months 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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Susana
2 months ago
C) There are missing values in the data.
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Elvera
2 months ago
B) There is not enough data to create a test set.
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Carlee
2 months ago
A) The data is unformatted.
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Elbert
2 months 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
3 months 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
3 months ago
I agree with Darrin. Cross-validation helps in estimating the model's performance when there are missing values.
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Darrin
3 months ago
I think we need to implement N-fold cross-validation when there are missing values in the data.
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Billye
3 months 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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Lynelle
1 months ago
D) There are categorical variables in the model.
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Lon
1 months ago
C) There are missing values in the data.
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Cletus
2 months ago
C) There are missing values in the data.
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Sabra
2 months ago
B) There is not enough data to create a test set.
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Gertude
2 months ago
A) The data is unformatted.
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Tammara
2 months ago
B) There is not enough data to create a test set.
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Bettina
2 months ago
A) The data is unformatted.
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Yuriko
3 months 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
3 months 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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Kristine
2 months ago
A) The data is unformatted.
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Rosann
2 months ago
B) I agree, cross-validation can help in such cases.
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Daron
2 months ago
C) There are missing values in the data.
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Rosita
3 months ago
B) There is not enough data to create a test set.
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