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

Limited Time Offer

25%

Off

Get Premium AIP-210 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

1 months 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.

upvoted 0 times

Loreta

2 days ago

I agree, option B makes sense for non-linear separable categories.

upvoted 0 times

...

Gregoria

1 months ago

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

upvoted 0 times

...