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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Ruthann

1 days 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

2 days 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

24 days 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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Sommer

26 days 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

27 days 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

28 days 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

1 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

1 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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