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CertNexus Exam AIP-210 Topic 1 Question 32 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 32
Topic #: 1
[All AIP-210 Questions]

Which of the following is NOT a valid cross-validation method?

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Suggested Answer: D

Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.


by Andra at Sep 17, 2024, 05:26 AM

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Leatha
4 months ago
Wait, isn't bootstrapping when I need to start a new business? These options are really mixing up my machine learning and entrepreneurship!
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Gianna
3 months ago
D) Stratification
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Moon
3 months ago
C) Leave-one-out
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Teddy
4 months ago
B) K-fold
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Zack
4 months ago
A) Bootstrapping
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Kerry
5 months ago
I'm not sure, but I think Bootstrapping is actually a valid cross-validation method because it involves resampling with replacement.
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Allene
5 months ago
I disagree, I believe the answer is A) Bootstrapping.
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Kerry
5 months ago
I think the answer is D) Stratification.
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Theola
5 months ago
Stratification? Sounds like a fancy way to separate my data into social classes. I'll pass on that one.
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Bette
5 months ago
K-fold? More like K-confusing if you ask me. I'll stick to the classics like leave-one-out.
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Deeann
4 months ago
Stratification is important for maintaining class balance in the data.
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Craig
4 months ago
I find bootstrapping to be quite useful as well.
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Alberto
4 months ago
Yeah, K-fold can be a bit confusing to implement.
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Dawne
4 months ago
I prefer leave-one-out too, it's simpler.
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Gerald
5 months ago
Bootstrapping? Isn't that what I do when I need to pull myself up by my own data-driven straps?
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Vicente
4 months ago
D) Stratification
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Maricela
4 months ago
C) Leave-one-out
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Johnna
4 months ago
B) K-fold
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Stevie
4 months ago
A) Bootstrapping
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Clemencia
4 months ago
That's right, bootstrapping is a way to estimate the sampling distribution of a statistic!
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Santos
4 months ago
D) Stratification
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Veronica
4 months ago
C) Leave-one-out
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Tomoko
4 months ago
B) K-fold
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Hildegarde
4 months ago
A) Bootstrapping
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Norah
4 months ago
Haha, no, bootstrapping in statistics is a resampling method!
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Dorsey
4 months ago
D) Stratification
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Cherry
4 months ago
C) Leave-one-out
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Yasuko
4 months ago
B) K-fold
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Hui
5 months ago
A) Bootstrapping
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