CertNexus Exam AIP-210 Topic 6 Question 15 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 15
Topic #: 6

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 Leatha at Apr 10, 2024, 03:25 AM

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Kent

6 months ago

That makes sense. In that case, a polynomial kernel could help capture complex relationships between highly correlated features.

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Marta

6 months ago

I believe using a polynomial kernel could also be helpful when there is high correlation among the features.

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Janey

6 months ago

Yes, that's a good point. It might be a trade-off between accuracy and computational time.

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Kent

6 months ago

I agree with Non-linearly separable categories could benefit from a polynomial kernel.

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Wai

6 months ago

But wouldn't using a polynomial kernel also increase computational time?

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Janey

6 months ago

I think using a polynomial kernel might help when the categories of the dependent variable are not linearly separable.

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Lili

7 months ago

I'm not sure, but I think high correlation among the features can be addressed by other techniques. Maybe using a polynomial kernel in that case is not the best approach.

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Kenda

7 months ago

But what about when there is high correlation among the features? Wouldn't a polynomial kernel help in that situation too?

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Ruthann

7 months ago

I agree with Lili. If the data points are not linearly separable, a polynomial kernel can help capture the non-linear relationships.

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Lili

7 months ago

I think using a polynomial kernel might help when the categories of the dependent variable are not linearly separable.

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Mari

8 months ago

I'm with Candidates 1 and 2 on this one. The polynomial kernel is all about that non-linear magic, and that's exactly what we need when the data doesn't play nice with linear models. As for the other options, they just don't seem as relevant to the question at hand.

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Tamala

7 months ago

I think we should go with a polynomial kernel then.

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Carri

7 months ago

Definitely, linear models can't handle those non-linear relationships.

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Rodrigo

7 months ago

I agree with Candidates 1 and 2 too. Non-linear magic is the way to go.

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Sommer

8 months ago

Haha, Beata, you always know how to lighten the mood. But in all seriousness, I think option B is the way to go here. The non-linear nature of the polynomial kernel is the key benefit when the categories are not linearly separable.

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Beata

8 months ago

Haha, I can already imagine the debate we're going to have about this one. Guys, let's not forget that the polynomial kernel is also great for adding a little spice to our models. Who doesn't love a good polynomial curve, am I right?

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Brynn

8 months ago

Ooh, this is a tricky one. I was thinking option D might be correct, since the polynomial kernel can help capture interactions between features. But I suppose that's not the primary reason to use it in this case.

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Adell

8 months ago

I agree with Pamella. The polynomial kernel can help capture non-linear relationships in the data, which is crucial when the categories are not linearly separable. Saving computational time or having a Gaussian distribution of the dependent variable are not directly related to the use of the polynomial kernel.

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Pamella

8 months ago

Hmm, this question seems to be testing our understanding of the fundamentals of support-vector machines. The polynomial kernel is often used when the data is not linearly separable, so I'd say option B is the correct answer.

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