CertNexus Exam AIP-210 Topic 2 Question 34 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 34
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 Malinda at Nov 16, 2024, 11:36 AM

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Tamar

2 days ago

Wait, the distribution of the dependent variable is Gaussian? Does that even matter for a support-vector machine? I'd have to go with option B as well.

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Rory

7 days ago

But wouldn't it also be useful when there is high correlation among the features?

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Mitsue

17 days ago

I agree with Gregoria. It can help capture non-linear relationships in the data.

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Twanna

17 days ago

I'm with Jimmie on this one. Option A is just silly. If you want to save time, you're better off using a linear kernel. Option B is definitely the right choice here.

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Jimmie

18 days ago

Haha, saving computational time? That's a good one. Polynomial kernels are all about complexity, not efficiency! Option B is the way to go.

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Truman

20 days ago

Hmm, I was considering option D, but I guess that's more about feature selection and dimensionality reduction. Option B makes more sense for the kernel choice.

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Malissa

21 days ago

I think option B is the correct answer. Using a polynomial kernel can help with non-linear decision boundaries and improve the model's ability to separate the categories.

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Gregoria

22 days ago

I think using a polynomial kernel would be helpful when the categories are not linearly separable.

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