CertNexus Exam AIP-210 Topic 2 Question 27 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 27
Topic #: 2

You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?

AWhen it is necessary to save computational time.

BWhen the categories of the dependent variable are not linearly separable.

CWhen the distribution of the dependent variable is Gaussian.

DWhen there is high correlation among the features.

Show Suggested Answer

Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.

by Gladys at Sep 11, 2024, 01:04 AM

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Dell

2 months ago

B definitely seems like the best answer here. I mean, who doesn't love a good ol' polynomial kernel to spice up their support-vector machine? Classic machine learning move.

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Luisa

24 days ago

D) When there is high correlation among the features.

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Rozella

25 days ago

C) When the distribution of the dependent variable is Gaussian.

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Vincenza

26 days ago

B) When the categories of the dependent variable are not linearly separable.

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Cristy

1 months ago

A) When it is necessary to save computational time.

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Sharen

2 months ago

Haha, this exam question is like a trick question! I'm going to go with B. Gotta love those non-linear decision boundaries, am I right?

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Gary

2 months ago

Agreed, B it is for sure.

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Jeannine

2 months ago

Definitely! Polynomial kernel can handle that well.

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Jina

2 months ago

I think B is the way to go. Non-linear separability is key.

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Titus

3 months ago

But wouldn't it increase computational time?

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Kristeen

3 months ago

I agree with Mee. It helps capture non-linear relationships in the data.

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Mee

3 months ago

Because when the categories of the dependent variable are not linearly separable, a polynomial kernel can help improve the prediction.

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Dallas

3 months ago

I'm not sure about this one. I would have guessed D, since a polynomial kernel can help handle correlated features. But B makes sense too. Hmm, tricky question!

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Zachary

1 months ago

That's true, it really depends on the data and the problem at hand. It's always good to consider all options.

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Noel

1 months ago

Actually, both B and D could be valid reasons to use a polynomial kernel. It depends on the specific situation.

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Annelle

2 months ago

I see your point, but I believe D is the better choice. It helps with high correlation among the features.

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Wilda

2 months ago

I think B is the correct answer. A polynomial kernel can help when the categories are not linearly separable.

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Inocencia

3 months ago

I think B is the correct answer. A polynomial kernel can help capture non-linear relationships in the data, which is useful when the categories are not linearly separable.

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