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Microsoft Exam DP-100 Topic 2 Question 108 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 108
Topic #: 2
[All DP-100 Questions]

You are solving a classification task.

You must evaluate your model on a limited data sample by using k-fold cross validation. You start by

configuring a k parameter as the number of splits.

You need to configure the k parameter for the cross-validation.

Which value should you use?

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Suggested Answer: B

by Amie at Jul 18, 2024, 07:05 PM

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Noble
3 months ago
Wait, is k-fold cross-validation the new crypto trend? I'm going to invest all my life savings in it!
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Janae
3 months ago
0.9? Hmm, looks like someone's trying to game the system. Nice try, but k-fold cross-validation requires whole numbers!
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Sabina
3 months ago
0.5? What is this, a discount voucher? The k-value has to be a positive integer, folks!
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Art
2 months ago
Sounds good, k=10 it is for the cross-validation.
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Jerlene
2 months ago
Let's go with k=10 then.
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Marylyn
2 months ago
You're right, k has to be a positive integer for cross-validation.
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Viola
2 months ago
D) k=0.9
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Coletta
2 months ago
C) k=0.5
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Izetta
2 months ago
B) k=10
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Ivette
2 months ago
A) k=1
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Alberto
3 months ago
10 is the way to go! It's a good balance between computational cost and model evaluation accuracy.
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Belen
2 months ago
10 is the way to go! It's a good balance between computational cost and model evaluation accuracy.
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Gwenn
2 months ago
D) k=0.9
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Sonia
2 months ago
C) k=0.5
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Shaun
3 months ago
B) k=10
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Derick
3 months ago
A) k=1
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Cyril
4 months ago
A k-value of 1 for k-fold cross-validation? Really? That's just plain old holdout validation, not cross-validation at all!
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Tonette
2 months ago
D) k=0.9
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Cherry
2 months ago
C) k=0.5
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Delmy
3 months ago
B) k=10
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Virgie
3 months ago
A) k=1
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Meaghan
4 months ago
I'm not sure, but I think k=10 provides a good balance between bias and variance in the model evaluation.
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Bette
4 months ago
I agree with Kirby, k=10 is a common choice for k-fold cross validation.
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Kirby
4 months ago
I think we should use k=10 for cross-validation.
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